Zeina Azmeh
email: azmeh@lirmm.fr

Jean-Rémy Falleri
email: falleri@lirmm.fr

Marianne Huchard
email: huchard@lirmm.fr

Chouki Tibermacine
email: tibermacin@lirmm.fr

Automatic Web Service Tagging Using Machine Learning and WordNet Synsets

Keywords: Tags, web services, text mining, machine learning

The importancy of Web services comes from the fact that they are an important means to realize SOA applications. Their increasing popularity caused the emergence of a fairly huge number of services. Therefore, finding a particular service among this large service space can be a hard task. User tags have proven to be a useful technique to smooth browsing experience in large document collections. Some service search engines proposes the facility of service tagging. It is usually done manually by the providers and the users of the services, which can be a fairly tedious and error prone task. In this paper we propose an approach for tagging Web services automatically. It adapts techniques from text mining and machine learning to extract tags from WSDL descriptions. Then it enriches these tags by extracting relevant synonyms using WordNet. We validated our approach on a corpus of 146 services extracted from Seekda.

Introduction

Service-oriented architectures (SOA) are achieved by connecting loosely coupled units of functionality. The most common implementation of SOA uses units of functionality invokable though Internet, called Web Services. Using SOA, a developer can quickly build a complex software by using already available Web services. One of the main tasks is therefore to find the relevant Web services to use in the software. With the increasing interest toward SOA, the number of existing Web services is dramatically growing. Finding a particular service among this huge amount of services is becoming a time-consuming task.

Web services are usually described with a standard XML-based language called WSDL. The WSDL format has been designed to be processed automatically by programs, but it includes a documentation part that can be filled with a text indicating to the user what the service do. Unfortunately, this documentation part is often not filled by the creators of the services. In this case, the potential users of the service spend time to understand its functionality and to

France Télécom R&D has partially supported this work (contract CPRE 5326).

decide whether or not they will use it. Moreover, it is common that a user has finally selected a service but finds out that in fact this service is irrelevant. When this case occurs, the user might want to easily get a list of services offering a similar functionality. Tagging is a mechanism that has been introduced in search engines and digital libraries to fulfill exactly this objective.

Tagging is the process of describing a resource by assigning some relevant keywords (tags) to it. The tagging process is usually done manually by the users of the resource to be tagged. Tags are useful when browsing large collections of documents. Indeed, unlike with traditional hierarchical categories, documents can be assigned an unlimited number of tags. It allows cross-browsing between the documents. Seekda1 , one of the main service search engines, already allows its users to tag its indexed services. Tags are also useful to have a quick understanding of a particular service. Moreover, since tags are words particularly important for the services, they are a good basis for other important tasks, like service classification or clustering.

In this paper, we present an approach that automatically extract a set of relevant tags from a WSDL service description, documented or not. We use a corpus of user-tagged services to learn how to extract relevant tags from untagged service descriptions. Our approach relies on text mining techniques in order to extract candidate tags out of a description, and machine learning techniques to select relevant tags among these candidates. The extracted set of tags is then enriched with semantically related tags using the WordNet ontology [START_REF] Fellbaum | WordNet: An Electronic Database[END_REF]. We have validated this approach on a corpus of 146 user-tagged Web services extracted from Seekda. Results show that this approach is significantly more efficient than the traditional (but fairly efficient) tfidf weight (explained in Section 2.1).

The remaining of the paper is organized as follows. Section 2 introduces the context of our work. Then, Section 3 details our tag extraction process. Section 4 presents a validation of this process and discusses the obtained results. Before concluding and presenting the future work, we describe the related work in Section 5.

Context of the Work

Our work focuses on extracting tags from service descriptions. In the literature, we found a similar problem: keyphrase extraction. Keyphrase extraction aims at extracting important and relevant short phrases from a plain-text document. It is mostly used on journal articles or on scientific papers in order to smooth browsing and indexation of those documents in digital libraries. Before starting our work, we analyzed one assessed approach that performs keyphrase extraction: Kea [START_REF] Frank | Domain-specific keyphrase extraction[END_REF] (Section 2.1). After this analysis, we concluded that a straightforward application of this approach is not possible on service descriptions instead of plain-text documents (Section 2.2).

Description of Kea

Kea [START_REF] Frank | Domain-specific keyphrase extraction[END_REF] is a keyphrase extractor for plain-text documents. It uses a Bayesian classification approach. Kea has been validated on several corpora [START_REF] Jones | Human evaluation of kea, an automatic keyphrasing system[END_REF][START_REF] Jones | Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications[END_REF] and has proven to be an efficient approach. It takes a plain-text document as input.

From this text, it extracts a list of candidate keyphrases. These candidates are the k i=1 k-grams of the text. For instance, let us consider the following sample document: "I am a sample document". The candidate keyphrases extracted if k = 2 are: (I,am,a,sample,document,I am,am a,a sample,sample document). To choose the most adapted value of k for the particular task of extracting tags from WSDL files, we made some measurments and found that 86% of the tags are of length 1. It clearly shows that one word tags are assigned in the vast majority of the cases. Therefore we will fix k = 1 in our approach (meaning that we are going to find one word length tags). Nevertheless, our approach, like Kea, is easily generalizable to extract tags of length k.

Kea then computes two features on every candidate keyphrase. First, distance is computed, which is the number of words that precede the first observation of the candidate divided by the total number of words of the document. For instance, for the sample document, distance(am a) = 1 5 . Second, tfidf, a standard weight in the information retrieval field, is computed. It measures how much a given candidate keyphrase of a document is specific to this document. More formally, for a candidate c in a document Kea uses a naive Bayes classifier to classify the different candidate keyphrases using the two previously described features. The authors showed that this type of classifier is optimal [START_REF] Domingos | On the optimality of the simple bayesian classifier under zero-one loss[END_REF] for this kind of classification problem. The two classes in which the candidate keyphrases are classified are: keyphrase and not keyphrase. Several evaluations on real world data report that Kea achieve good results [START_REF] Jones | Human evaluation of kea, an automatic keyphrasing system[END_REF][START_REF] Jones | Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications[END_REF]. In the next section, we will describe how WSDL files are structured and highlight why the Kea approach is not directly applicable on this kind of data.

WSDL service descriptions

The documents from which we intend to extract tags are service descriptions in the WSDL format. This format is XML-based and aims at describing the different elements involved in a web service. Those elements are: services, ports, port types, bindings, types and messages. Their descriptions always come with a name, called identifier (example: MyWeatherService, ComputeExchangeRatePort). They can optionally come with a plain-text documentation. Figure 3 (left) shows the general outline of a WSDL file.

One simple idea to extract tags from services would be to use Kea on their plain-text documentations. Unfortunately, an analysis of our service corpus (see Several pre-processing and text-mining techniques are required. Moreover, the previous described feature (tfidf and distance) are not easy to adapt on words coming from a service descriptions. First because WSDL deals with two categories of words (the one coming from the documentation and the one coming from the identifiers) that are not necessary related. Second because the distance feature is meaningless on the identifiers, which are defined in an arbitrary order.

Tag Extraction Process

Similarly to Kea, we model the tag extraction problem as the following classification problem: classifying a word into one of the two tag and no tag classes. Our overall process is divided into two phases: the training phase and the tag extraction phase. Figure 1 summarizes the behavior of the training phase. In this phase we dispose of a corpus of WSDL files and associated tags. Since we did not find such a publicly available corpus, we created one by using data from Seekda. The creation of this training corpus is described in Section 3.1. From this training corpus, we first extract a list of candidate words by using text-mining techniques. The extraction of these candidates is described in Sections 3.2 and 3.3. Then several features are computed on every candidate. A feature is a common term in the machine learning field. It can be seen as an attribute that can be computed on the candidates (for instance the frequency of the words in their WSDL file). Finally, since manual tags are assigned to those WSDL files, we use them to classify the candidate words coming from our WSDL files. Using this set of candidate words, computed features and assigned classes, we train a classifier.

New WSDL file word 1 + features word 2 + features word n + features

Unclassified candidate words

Trained classifier + tag 1 tag 2 tag k

Tags

Fig. 2. The tag extraction phase

This trained classifier will then be used to classify words coming from subsequent WSDL files during the tag extraction phase.

Figure 2 describes the tag extraction phase. First, like in the training phase, a list of candidate words is extracted from an untagged WSDL file. The same features as in the training phase are then computed on those words. The only difference with the training phase is that we do not know in advance which of those candidates are true tags. Therefore we use the previously trained classifier to automatically perform this classification. Finally the tags extracted from the WSDL file are the words that have been classified in the tag class. It is noteworthy to remark that the training phase is only performed once, while the tag extraction phase can be applied an unlimited number of times.

Creation of the training corpus

As explained above, our approach requires a training corpus, denoted by T . Since we want to extract tags from WSDL files, T has to be a set of couples (wsdl, tags), with wsdl a WSDL file, and tags a set of corresponding manually assigned tags. We were not aware of such a publicly available corpus. Therefore we decided to create one using data from Seekda. Indeed, Seekda allows its users to manually assign tags to its indexed services. We created a program that crawls on the Seedka services and extracts the WSDL files together with the user tags. To ensure that the services of our corpus were significantly tagged, we only retain the WSDL files that have at least five tags. Using this program, we extracted 150 WSDL files. Then, we removed from T the WSDL files that triggered parsing errors. Finally, we dispose of a training corpus containing 146 WSDL files together with their associated tags.

To clean the tags of the training corpus, we performed the three following operations:

-We removed the non alpha numeric characters from the tags (we found several tags like _onsale or :finance), -We removed a meaningless and highly frequent tag (the _unkown tag), -We divided the tags with length n > 1 into n tags of length 1, in order to have only tags of length 1 (the reason has been explained in section 2.1). The length of a tag is defined as the number of words composing this tag.

Finally, we dispose of a corpus of 146 WSDL files and 1393 tags (average of 9.54 tags per WSDL). An analysis of T shows that about 35% of the user tags are already contained in the WSDL files. Now that we have this training corpus, we will shortly describe the approach upon which our work is built.

Global documentation

Identifier set

Fig. 3. WSDL pre-processing

Pre-processing of the WSDL files

As we have seen before, a WSDL file contains several element definitions optionally containing a plain-text documentation. The left side of figure 3 shows such a data structure. In order to simplify the WSDL XML representation in a format more suitable to apply text mining techniques, we decided to extract two documents from a WSDL description:

-A set of couples (type, ident) representing the different elements defined in the WSDL. We have type ∈ (Service,Port,PortType,Message,Type,Binding) the type of the element and ident the identifier of the element. We call this set of couples the identifier set. -A plain text containing the union of the plain-text documentations found in the WSDL file, called the global documentation.

This pre-processing operation is summarized in the figure 3.

Selection of the candidate tags

As seen in the previous section, we dispose now of two different sources of information for a given WSDL: an identifier set and a global documentation. Unfortunately, those data are not yet usable to compute meaningful metrics. Firstly because the identifiers are names of the form MyWeatherService, and therefore are very unlikely to be tags. Secondly because this data contains a lot of obvious useless tags (like the you pronoun). Therefore, we will now apply several text-mining techniques on the identifier set and the global documentation.

Figure 4 shows how we process the identifier set. Here is the complete description of all the performed steps:

1. Identifier type filtering: during this step, the couples (type,ident) where type ∈ (PortType,Message,Binding) are discarded. We applied this filtering because very often, the identifiers of the elements in those categories are duplicated from the identifiers in the others categories. To split ident into several tokens, we created a tokenizer that uses common clues in software engineering to split the words. Those clues are for instance a case change, or the presence of a non alpha-numeric character. 3. POS tagging: during this step each couple (type, tokens) previously computed is replaced by a couple (type, ptokens). ptokens is a set of couples (token i , pos i ) derived from tokens where token i is a token from tokens and pos i the part-of-speech corresponding to this token. We used the tool tree tagger [START_REF] Schmid | Probabilistic part-of-speech tagging using decision trees[END_REF] Figure 5 shows how we process the global documentation. Here is the complete description of all the performed steps:

1. HTML tags removal: the HTML tags (words begining by < and ending by >) are removed from the global documentation. 2. POS tagging: similar to the POS tagging step applied to the identifier set. 3. POS filtering: similar to the POS filtering step applied to the identifier set.

HTML tags removal

Union of the documentations of our weather service.

[ Union of the documentations of our <b>weather</b> service.

POS tagging

POS filtering

Fig. 5. Processing of the global documentation

The union of the remaining words in the identifier set and in the global documentation are our candidate tags. When defining those processing operations, we took great care that no correct candidate tags (i.e. a candidate tag that is a real tag) of the training corpus have been discarded. The next section describes how we adapted the Kea features to these candidate tags.

Computation of the features

After having applied our text mining techniques on the identifier set and the global documentation, we dispose now of different well separated words. Therefore we can now compute the tf idf feature. But words appearing in documentation or in the identifier names are not the same. We decided (mostly because it turns out to perform better) to separate the tf idf value into a tf idf ident and a tf idf doc which are respectively the tf idf value of a word over the identifier set and over the global documentation. Like in Kea, we used the method in [START_REF] Fayyad | Multi-interval discretization of continuous-valued attributes for classification learning[END_REF] to discretize those two real-valued features.

The distance feature still has no meaning over the identifier set, because the elements of a WSDL description are given in an arbitrary order. Therefore we decided to adapt it by defining five different features: in_service, in_port, in_type, in_operation and in_documentation. Those features take their values in the (true, f alse) set. A true value indicates that the word has been seen in an element identifier of the corresponding type. For instance in_service(weather) = true means that the word weather has been seen in a service identifier. in_documentation(weather) = true means that the word weather has been seen in the global documentation.

In addition of these features, we compute another feature called pos. We added this feature, not used in Kea, because it significantly improves the results. pos is simply the part-of-speech that has been assigned to the word during the POS tagging step. If several parts-of-speech have been assigned to the same word, we choose the one that has been assigned in the majority of the cases. The different values of pos are: NN (noun), NNS (plural noun), NP (proper noun),

Training and using the classifier

We applied the previously described technique to all the WSDL files of T .

In addition to the previously described features, we compute the is_tag feature over the candidates. This feature takes its values in the (true, f alse) set. is_tag(word) = true means that word has been assigned as a tag by Seekda users for its service description. We have serialized all those results in an ARFF file compatible with the Weka tool [START_REF] Witten | Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations[END_REF]. Weka is a machine learning tool that defines a standard format for describing a training corpus and furnish the implementation of many classifiers. One can use Weka in order to train a classifier or compare the performances of different classifiers regarding a given classification problem. Table 1 shows an extract of the ARFF file we produce. In this table, words are displayed for the sake of clarity, but in reality, they are not present in the ARFF file. The ARFF file only contains features. With this ARFF file, we used Weka to train a naive Bayes classifier, shown as optimal for our kind of classification task [START_REF] Domingos | On the optimality of the simple bayesian classifier under zero-one loss[END_REF]. This trained classifier can now be used in the tag extraction phase. As previously said, the beginning of this phase is the same as the one of the training phase. It means that the WSDL file goes through the previously described operations (pre-processing, candidates selection and features computation). Only this time, the value of the is_tag feature is not available. This value will be automatically computed by the previously trained classifier.

WordNet for semantically related tags

In our approach, the classifier that we built determines whether a word in a WSDL file is a tag or not. Thus, it extracts the tags appearing inside the WSDL files only. This way, we miss some other interesting tags like associated words or synonyms. In order to solve this issue, we used the WordNet lexical database [START_REF] Fellbaum | WordNet: An Electronic Database[END_REF]. In WordNet a word may be associated with many synsets (synonym sets), each corresponding to a different sense of a word.

Our corpus consists of 146 WSDL files, each of which is assigned two sets of tags: user tags and our automatically extracted tags. Our objective is to enrich each set of tags with semantically similar words extracted from WordNet. Thus, for each tag we identify the possible senses and the synonyms set related to each sense. We add the extracted synonyms to the corresponding set of tags, and we perform some experiments to evaluate the obtained tags, as we show in the next section.

Validation of the Proposed Work

This section provides a validation of our technique on real world data from Seekda. We conduct experiments in which we assess the precision and recall of our trained classifier.

Methodology:

We carried out our experiments on three stages. In the first one, the trained classifier is applied on the training corpus T and its output is compared with the tags given by Seekda users (obtained as described in Section 3.1).

After having conducted the first experiment, a manual assessment of the tags produced by our approach revealed that many tags not assigned by the user seemed highly relevant. This phenomenon has also been observed in several human evaluations of Kea [START_REF] Jones | Human evaluation of kea, an automatic keyphrasing system[END_REF][START_REF] Jones | Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications[END_REF], that inspired our approach. It occurs because tags assigned by the users are not the absolute truth. Indeed, it is very likely that users have forgotten many relevant tags, even if they were in the service description. To show that the real efficiency of our approach is better than the one computed in the first experiment, we perform a second experiment. In this experiment, we manually augmented the user tags of our corpus with additional tags we found relevant and accurate by analyzing the WSDL descriptions of the services. In the final experiment, we enriched the user tags as well as our automatically extracted tags with semantically related tags using WordNet. . The recall is therefore computed over the tags assigned by Seekda users that are present in the descriptions of concerned services. We did not compute the recall for the WordNet extracted tags, because these tags may not be present in the WSDL descriptions.

Metrics

Evaluation: Figure 6 (left) gives results for the first experiment where the output of the classifier is compared with the tags of Seekda users, while in Figure 6 (right), enriched tags of Seekda users are used in the comparison (curated corpus). In this figure, our approach is called ate (Automatic Tag Extraction). To clearly show the concrete benefits of our approach, we decided to include in these experiments a straightforward (but fairly efficient) technique. This technique, culated the precision value, considering these new sets of enriched user tags and automatically extracted tags. The precision value has increased by 9%, reaching the value of 89% of correctness. Thus, using the WordNet has improved the precision value and enriched the services with tags that are not necessarily present in the WSDL descriptions.

Threats to validity:

Our experiments use real world services, obtained from the Seekda service search engine. Our training corpus contains services extracted randomly with the constraint that they contain at least 5 user tags. We assumed that Seekda users assign correct tags. Indeed, our method admits some noise but would not work if the majority of the user tags were poorly assigned. In the second experiment, we manually added tags we found relevant by examining the complete description and documentation of the concerned services. Unfortunately, since we are not "real" users of those services, some of the tags we added might not be relevant.

Related Work

In this section, we will present the related work according to two fields of research: keyphrase extraction and web service discovery.

Keyphrase extraction and assignment

According to [START_REF] Turney | Coherent keyphrase extraction via web mining[END_REF], there are two general approaches that are able to supply keyphrases for a document: keyphrase extraction and keyphrase assignment. Both approaches are using supervised machine learning approaches, with training examples being documents with manually supplied keyphrases.

Keyphrase assignment:

In the keyphrase assignment approach, a list of predefined keyphrases is treated as a list of classes in which the different documents are classified. Text categorization techniques are used to learn models for assigning a class (keyphrase) to a document. Two main approaches of this category are [START_REF] Dumais | Inductive learning algorithms and representations for text categorization[END_REF][START_REF] Leung | A statistical learning approach to automatic indexing of controlled index terms[END_REF].

Keyphrase extraction:

In the keyphrase extraction approach, a list of candidate keyphrases are extracted from a document and classified into the classes keyphrase and not keyphrase. There are two main approaches that fall in this category: one using a genetic algorithm [START_REF] Turney | Learning algorithms for keyphrase extraction[END_REF] and one using a naive Bayes classifier (Kea [START_REF] Frank | Domain-specific keyphrase extraction[END_REF]).

Web service discovery

Web service discovery is a wide research area with many underlying issues and challenges. A quick overview of some of the works can be acquired from [START_REF] Brockmans | Service-finder deliverable d4.1[END_REF][START_REF] Lausen | Survey of current means to discover web services[END_REF] 2 . Here, we describe a selection of works, classified using their adapted techniques. 2 The second one is edited by the responsible of Seekda's technical infrastructure Using machine learning techniques: Many approaches adapt techniques from machine learning field, in order to discover and group similar services. In [START_REF] Crasso | Awsc: An approach to web service classification based on machine learning techniques[END_REF][START_REF] Heß | Learning to attach semantic metadata to web services[END_REF], service classifiers are defined depending on sets of previously categorized services. Then the resulting classifiers are used to deduce the relevant categories for new given services. In case there were no predefined categories, unsupervised clustering is used. In [START_REF] Ma | Efficiently finding web services using a clustering semantic approach[END_REF], CPLSA approach is defined that reduces a services set then cluster it into semantically related groups.

Using service matching techniques:

In [START_REF] Lu | Web service search: Who, when, what, and how[END_REF], a web service broker is designed relying on approximate signature matching using xml schema matching. It can recommend services to programmers in order to compose them. In [START_REF] Günay | Structural and semantic similarity metrics for web service matchmaking[END_REF], a service request and a service are represented as two finite state machines then they are compared using various heuristics to find structural similarities between them. In [START_REF] Dong | Similarity search for web services[END_REF], the Woogle web service search engine is presented, which takes the needed operation as input and searches for all the services that include an operation similar to the requested one. In [START_REF] Bouillet | A folksonomy-based model of web services for discovery and automatic composition[END_REF], tags coming from folksonomies are used to discover and compose services.

Using vector space model techniques:

The vector space model is used for service retrieval in several existing works as in [START_REF] Platzer | A vector space search engine for web services[END_REF][START_REF] Wang | Semantic structure matching for assessing web service similarity[END_REF][START_REF] Crasso | Query by example for web services[END_REF]. Terms are extracted from every WSDL file and the vectors are built for each service. A query vector is also built, and similarity is calculated between the service vectors and the query vector. This model is sometimes enhanced by using WordNet, structure matching algorithms to ameliorate the similarity scores as in [START_REF] Wang | Semantic structure matching for assessing web service similarity[END_REF], or by partitioning the space into subspaces to reduce the searching space as in [START_REF] Crasso | Query by example for web services[END_REF].

Using formal concept analysis techniques:

A collection of works [START_REF] Aversano | Using concept lattices to support service selection[END_REF][START_REF] Peng | Management and retrieval of web services based on formal concept analysis[END_REF][START_REF] Azmeh | Wspab: A tool for automatic classification & selection of web services using formal concept analysis[END_REF], adapt the formal concept analysis method to retrieve web services more efficiently. Contexts obtained from service descriptions are used to classify the services as a concept lattice. This lattice helps in understanding the different relationships between the services, and in discovering service substitutes.

Conclusion and Future Work

With the emergence of SOA, it becomes important for developers using this paradigm to retrieve Web services matching their requirements in an efficient way. By using Web service search engines, these developers can either search by keywords or navigate by tags. In the second case, it is necessary that the tags characterize accurately this service. Our work contributes in this direction and introduces a novel approach that extracts tags from Web service descriptions and enrich them using the WordNet ontology. This approach combines and adapts text mining as well as machine learning techniques. It has been experimented on a corpus of user-tagged real world Web services. The obtained results demonstrated the efficiency of our automatic tag extraction process, and the enrichment of semantically similar tags. The use of WordNet has improved the precision of the returned tags and enriched the services with tags that are not necessarily present in the WSDL descriptions. The proposed work is useful for many purposes. First, the automatically extracted tags can assist the users who are tagging a given service, or to "bootstrap" tags on untagged services. They are also useful to have a quick understanding of a service without reading the whole description. They can also be used to help in building domain ontologies like in [START_REF] Sabou | Learning domain ontologies for web service descriptions: an experiment in bioinformatics[END_REF] [START_REF] Guo | Learning ontologies to improve the quality of automatic web service matching[END_REF], also in tasks such as service clustering (for instance by measuring the similarity of the tags of two given services), or classification (for instance by defining association rules between tags and categories). One of our perspectives is to work on extracting composed tags, which consist of more than one word. A one-word tag is sometimes insufficient to describe some concepts (for example exchange rate or Web 2.0 ).

  d, tf idf (c, d) = tf (c, d) × idf (c). The metric tf (c, d) (term frequency) corresponds to the frequency of the term c in d. It is computed with the following formula: tf (c, d) = occurences of c in d size of d . The metric idf (c) (inverse document frequency) measure the general importance of the term in a corpus D. idf (c) = log( |D| |{d: c ∈d}| ).

Fig. 4 .

 4 Fig. 4. Processing of the identifiers

:

  In the evaluation, we used precision and recall. First, for each web service s ∈ T , where T is our training corpus, we consider: A the set of tags produced by the trained classifier, M the set of the tags given by Seekda users and W the set of words appearing in the WSDL. Let I = A ∩ M be the set of tags assigned by our classifier and Seekda users. Let E = M ∩ W be the set of tags assigned by Seekda users present in the WSDL file. Then we define precision(s) = |I| |A| and recall(s) = |I| |E| , which are aggregated in precision(T ) = s∈T precision(s) |T | and recall(T ) = s∈T recall(s) |T |

Table 1 .

 1 Extract of the ARFF file Word T F IDF id T F IDF doc IN _SERV ICE . . . IN _DOC P OS IS_T AG

	Weather [0, 0.01] ]0.01, 0.04]	×	N N	×
	Location ]0.03, 0.1] ]0.04, 0.15]	×	JJ
	Code ]0.03, 0.1] ]0.01, 0.04]	×	V V
	NPS (plural proper noun), JJ (adjective), JJS (plural adjective), VV (verb),
	VVG (gerundive verb), VVD (preterit SYM (symbol).

http://www.seekda.com

Fig. 6. Results on the original and manually curated Seekda corpus called tfidf in Figure 6, consists in selecting, after the application of our textmining techniques, the five candidate tags with the highest tfidf weight.

In Figure 6 (left), the precision of ate is 0.48. It is a significant improvement compared to the tfidf method that achieves only a precision of 0.28. Moreover, there is no significant difference between the recall achieved by the two methods. To show that the precision and recall achieved by ate are not biased by the fact that we used the training corpus as a testing corpus, we performed a 10 folds cross-validation. In a 10 folds cross-validation, our training corpus is divided in 10 parts. One is used to train a classifier, and the 9 other parts are used to test this classifier. This operation is done for every part, and then, the average recall and precision are computed. The results achieved by our approach using cross-validation (precision = 0.44 and recall = 0.42) are very similar to those obtained in the first experiment.

In Figure 6 (right), we see that the precision achieved by ate in the second experiment is much better. It reaches 0.8, while the precision achieved by the tfidf method increases to 0.41. The recall achieved by the two methods remains similar. The precision achieved by our method in this experiment is good. Only 20% of the tags discovered by ate are not correct. Moreover, the efficiency of ate is significantly higher than tfidf.

Evaluation after using WordNet:

We enriched the tags sets with semantically similar words extracted using the WordNet, as described above. We recal-


Olivier Glassey
email: olivier.glassey@idheap.unil.ch

A Survey on Participation at Geneva's Constituent Assembly

Keywords: participation, constitution, eDemocracy, framework, case study

In February 2008 the people of Geneva voted in favor of a new Constitution to replace the current one, written in 1847 and considered by many to be out of line with today's society. The main objective of this research was to analyze participation during the process of writing a new Constitution. In the first part of this paper we set the context of our study and in the second we describe our research methodology and analysis framework. In the last section we describe our findings regarding actors and processes of participation and eParticipation, as well as underlying communication and coordination channels.

Introduction

Between November 2008 and February 2010 we surveyed the communication and coordination mechanisms of selected stakeholders in order to analyze participation and eParticipation at the Constituent Assembly in the Canton of Geneva in Switzerland. In previous work [START_REF] Glassey | Writing a New Constitution for Geneva: An Analysis of Participation Mechanisms[END_REF] we described our analysis framework and presented preliminary results of this ongoing research project. This current paper contains the complete results of our research.

This first section describes the context in which this study takes place, i.e. the work of a Constituent Assembly to write a new Constitution for the Canton of Geneva. The second section explains our research methodology and analysis framework; in the last part of this paper we discuss our results.

Geneva's Constitution

Geneva is a republic since 1535 when the city became the capital of the Protestant Reformation. The first Constitution adopted in 1543 was largely based on the "Edits Civils" written by John Calvin. Although Geneva was a French department between 1798 (when it was invaded by Napoleon's army) and 1813, it was mostly independent until joining the Swiss Confederation in 1815.

In 1846 James Fazy led a revolution that overthrew the conservative government and subsequently wrote the 1847's Constitution that is still ruling the Canton, although it has been modified many times over that period. This text is now the oldest of the 26 Cantonal Constitutions in Switzerland and many believed that its language, structure and content are not adequate anymore [START_REF] Schmitt | Quelques réflexions comparatives à propos de l'élection d'une assemblée constituante à Genève[END_REF].

In 1999 the parliamentary group of the "Parti Radical" proposed a bill in order to completely revise Geneva's Constitution, but without success. In 2005 an association called "Une nouvelle Constitution pour Genève" (a New Constitution for Geneva) was set up. Its front man was a famous law professor, Andreas Auer, and its members came from all political parties and from the civil society. They were ready to launch a popular initiative requiring a new Constitution as the government was reluctant to do so, but after long negotiations a vote was organized. In February 2008 the people of Geneva accepted a constitutional law allowing for a new Constitution in the Canton.

Election of the Constituent Assembly

In October 2008 the people of Geneva elected 80 members of the new Constituent Assembly. This was no easy task for citizens as there were 530 candidates and 18 lists to choose from. Half of these lists were presented by traditional political parties and the other nine lists represented heterogeneous interest groups (business associations, home-owners, women, retired people, and so on). Funding for the campaign was also very heterogeneous: from 5'000 Swiss Francs (about 3'500 Euros) for the women's list to 200'000 (140'000 Euros) for the business associations' list.

The quorum for a list to be elected was initially 7%, but the Parliament lowered it to 3% in order to have a wider participation. However one cannot say that the initial members of this Assembly were really representative of Geneva's people: only 14 women were elected (although two elected men resigned in order to leave their position to women from the same left-wing party) and the average age of members was 56. Furthermore only three lists outside traditional parties made the quorum:

• The lobby of pensioned people (Avivo) got 9 seats; it must be said that Christian Grobet, the leader of this list, was a member of various legislative and executive authorities in Geneva from 1967 until 2005, thus this list is not completely "outside" political parties. • The g[e]'avance list represented business and employers' lobbies and it was attributed 6 seats. • The FAGE (Federation of Geneva's Associations) is the umbrella organization of 480 associations of all types (parents, culture, human rights, ecology, Attac, pacifism, consumers, social integration, gays, development, etc.); the associations' list obtained almost 4% of the votes (with a quorum at 3%) and thus obtained 3 seats. The participation rate being of 33 % (about 10% less than the average participation), one can conclude that giving Geneva a new Constitution was not a popular issue and that only "traditional" or "politicized" voters accomplished their electoral duties. The political balance of the Constituent Assembly was also similar to the Parliament of Geneva: 43 seats for right wing parties and 37 for the left.

Research Methodology and Analysis Framework

This section describes the dimensions of participation, eParticipation, and eDemocracy that we build upon them in order to define our analysis framework.

Participation and eParticipation

According to [START_REF] Sanford | Characterizing eParticipation[END_REF] eParticipation is an emerging research area which lacks a clear literature base or research approach. In their review of the field, they identified and analyzed 99 articles that are considered to be highly relevant to eParticipation. [START_REF] Sanford | Characterizing eParticipation[END_REF] write in their introduction that governments seek to encourage participation in order to improve the efficiency, acceptance, and legitimacy of political processes. They identify the main stakeholders of participation as citizens, non-governmental organizations, lobbyists and pressure groups, who want to influence the political system, as well as the opinion forming processes. Various information and communication technologies (ICTs) are available for eParticipation: discussion forums, electronic voting systems, group decision support systems, and web logging (blogs). However traditional methods for citizen participation (charettes, citizens' juries or panels, focus groups, consensus conferences, public hearings, deliberative polls, etc.) are still very widely used and must be taken into account when studying eParticipation.

[4] defines eDemocracy as the use of information and communication technologies to engage citizens, to support the democratic decision-making processes and to strengthen representative democracy. She furthermore writes that the democratic decision-making processes can be divided into two main categories: one addressing the electoral process, including e-voting, and the other addressing citizen e-participation in democratic decisionmaking. [START_REF] Macintosh | Evaluating how eParticipation changes local democracy[END_REF] give a working definition of eParticipation as the use of ICTs to support information provision and "top-down" engagement, i.e. government-led initiatives, or "ground-up" efforts to empower citizens, civil society organizations and other democratically constituted groups to gain the support of their elected representatives.

There are many examples of surveys on eDemocracy, such as [START_REF] Ladner | From e-voting to smart-voting -e-Tools in and for elections and direct democracy in Switzerland[END_REF] who take the case of Switzerland where citizens are often called to the polls either to vote for parties and candidates or, even more often, to decide on direct-democratic votes at the three different political levels. In their paper on "smart-voting" they analyze what they call voting assistance applications, i.e. tools where citizens can compare their positions on various political issues to those of parties or candidates. They mention the Dutch "Stemwijzer" system, first introduced in 1998 and they provide in-depth information on the Swiss smartvote website.

Even if eParticipation is a relatively new research field, projects and tools are increasing thanks to governmental support [START_REF] Tambouris | A Framework for Assessing eParticipation Projects and Tools[END_REF]. Furthermore a number of research projects such as Demo-Net.org have been funded worldwide to pave the way.

Analysis Framework

Our main objective is to survey communication and coordination mechanisms for participation and eParticipation, thus we defined an analysis framework integrating two central variables:

• Communication and coordination channels.

• Levels of participation.

To investigate traditional and electronic communication channels we adapted the approach used by [START_REF] Mambrey | From Participation to e-Participation: The German Case[END_REF] for its case study on participation and eParticipation in Germany, where he used the three arenas of political communications defined by [START_REF] Habermas | The Theory of Communicative Action[END_REF]. Table 1 shows these three communications modes and the systems or actors involved in political communication, as well as the vectors used to carry this communication. We made a distinction between traditional participation vectors and ICT-enabled channels (eParticipation). As for our second variable, we relied on the five levels used by [START_REF] Tambouris | A Framework for Assessing eParticipation Projects and Tools[END_REF] in their framework to assess eParticipation:

• (e-)Informing

• (e-)Consulting • (e-)Involving • (e-)Collaborating • (e-)Empowerment
We do realize the limitations of such an approach, and we agree with [START_REF] Grönlund | ICT Is Not Participation Is Not Democracy -eParticipation Development Models Revisited[END_REF] who states that eParticipation analysis models are typically ladder type and share two assumptions: progress is equaled with more sophisticated use of technology, and direct democracy is seen as the most advanced democracy model. However we think it is useful to characterize the results of our survey.

In addition to these two main dimensions, we added the distinction proposed in the previous section by [START_REF] Macintosh | Evaluating how eParticipation changes local democracy[END_REF]: top-down initiated participation vs. bottom-up participation.

Finally we used two additional concepts in order to describe and characterize the context of participation and eParticipation:

• Process: we rely on the deliberation lifecycle defined by [START_REF] Passas | A Procedural Model for Public Deliberation[END_REF] in their comprehensive framework to analyze deliberative decision making: issue emergence, issue structuring, issue analysis, deliberation, decision, monitoring and evaluation. • Stakeholders: we describe them according to the generic stakeholders of eParticipation workflows defined by [START_REF] Colombo | Workflow Modeling for Participatory Policy Design: Lessons form Three European Regions[END_REF]: owners, decision-makers, practitioners, moderators and participants.

Selection of Stakeholders and Methodology

In order to apply our analysis framework, we selected a sample of stakeholders involved in institutional, mediated and informal communication:

• The "Parti Radical Genevois" (PRG) is a progressive right-wing party that has a long history in Geneva; it was born in 1841-42 during the first revolutionary movements in the Canton and it was led by James Fazy, the author of the 1847's Constitution that is still in effect today. We selected it because PRD is rather representative of traditional parties and political structures. • "Les Verts" are Geneva's green party. It was founded in 1983 by various members of environmental and anti-nuclear associations. The Green party is now a wellestablished party with, amongst others, two elected members of the executive government in Geneva (which comprises seven ministers). We decided to survey them because they are a newer party, created by members of the civil society and based on a more associative operational mode. • We already introduced the Federation of Geneva's Associations (FAGE) in section 1:

it is the umbrella organization of 480 associations. We integrated them in our study because they are very typical of networked communication and participation. • The "Tribune de Genève" (TDG): we chose it because it is Geneva's main printed newspaper and it furthermore provides a blog platform to its readers; most blogs related to the Constituent Assembly are hosted at the TDG. • The Communication Bureau of the Constituent Assembly: this is the official communication channel of the Assembly. • The Plenary Assembly, where deliberation and decision-making takes place. This survey is qualitative and based on two investigation methods:

• Periodical review of secondary sources: all identified websites, blogs, forums, wikis, and publicly available working documents related to the Constituent Assembly. • Semi-structured interviews with representatives of the stakeholders listed above. We used a standardized interview guide (with adaptations when necessary, e.g. when interviewing elected members or "outsiders"). It was based on the following questions:

• How do you prepare plenary sessions of the Constituent Assembly?

• How do you make decisions (e.g. regarding voting at plenary sessions)?

• How are you organized regarding participation / consultation of your members? Of citizens? • How do you manage documents and information produced by the Constituent Assembly? • How do you communicate around your work? • Do you use information and communication technology for your work (mail, wiki, office suites, document servers, blogs, etc.)? • Do you see any opportunities for eParticipation at the Constituent Assembly?

In this section the results of our survey are presented according to the dimensions we defined in our analysis framework. We will not go into the details of our analysis and we will try to highlight only the key findings.

Communication and Coordination Channels in Participation

We will use the dimensions defined in Table 1 to describe our findings in terms of communication and coordination channels.

On institutional participation: the Constitutional Assembly's first task was to define its own operational rules, as the constitutional law did not contain any information on execution. These rules defined several innovative possibilities (in comparison to standard parliamentary policies in Switzerland). They allowed several interesting participatory tools:

• Petitions: any person or group can submit a proposal to the Constituent Assembly under the form of a petition; petitions are transferred to the relevant thematic commission that then decides whether they want to take it into account. • Collective proposals: a proposal signed by at least 500 citizens has to be handled by the relevant thematic commission and should be answered in a chapter of the commission's report; at the time of writing, around 20 collective proposals were submitted. • Public hearings: the Assembly can hear any representative of the civil society or of interest groups, as well as members of the public sector from Geneva, and from other Cantons or countries; this concept of public hearing did not previously exist in Switzerland, where hearings are privately held in commissions. • Plenary Assembly: all members, commissions, groups or circles can make proposals and submit amendments. Minutes of the plenary sessions are published • Thematic commissions: 5 thematic commissions (fundamental rights, political rights, institutions, territory, roles of the State and finance) as well as a coordination commission were set up. They work on thematic content and can organize hearings (private or public) and they already provided preliminary reports. • Groups: all members elected on a list belong to a common group; each group received funding for a parliamentary assistant; an elected member that would quit his group could not take part in thematic commissions anymore; groups can be heard at the plenary assembly upon request. • Circles: elected members that have common interests can form a circle; circles must be formed of at least three members; members can be part of several groups; circles can be heard at the plenary assembly upon request; for the time being there are circles dedicated to sustainable development, youth, SMEs, and culture. Although several innovative participation channels were defined there is no eParticipation at the institutional level. However bottom-up participation is supported, mainly with collective proposals and petitions. Institutional communication is quite traditional, although press releases, minutes and thematic reports are published online.

On mediated participation:

• Parties: the "Parti Radical Genevois" has only one participation channel, the Caucus, where elected members and the party's leaders define their positions. "Les Verts" also have a Caucus operating in a similar way, and they additionally work with so-called "resources groups" where members of the party debate and make recommendations on given issues. The "FAGE" has set up several participation channels: colloquiums or meetings where any member of the 480 federated organizations can attend and make their opinion known; competence poles that are organized similarly to resources groups mentioned above; common objectives, where a minimal and common set of requirements for all 480 associations is defined. • Media: the local press, radio and TV provide minimal coverage of the Constituent Assembly, and it does not seem to raise much interest. However the blog platform of the "Tribune de Genève" is very active, with around 20 elected members and one group having their own blog. Furthermore two or three elected members comment the plenary sessions on Twitter; one of them even launched a contest to propose an introduction to the new Constitution. Last, the main source of information and place of exchange on the Constituent Assembly is a blog called "La Gazette de la Constituante" maintained by a journalist of the "Tribune de Genève". • Interest groups, associations: most of the collective proposals mentioned above were initiated and supported by these groups. Here again several interesting participation channels were developed, but eParticipation is still rather limited: one blog platform and a few "tweets". In parties we found mainly topdown supported participation, with the exception of the FAGE where bottom-up participation is made possible through the possibility of colloquium's attendance for any member of any represented association.

On informal participation: few citizens comment on the blogs and follow the "tweets" mentioned above. Participation channels remain very traditional with the possibility of submitting a petition or of signing a collective proposal.

Levels of Participation

In Fig. 1 we positioned our various stakeholders regarding their participation level and their participation approach. This graph is given as an indication only, as it was not created on the basis of quantitative indicators, but rather on the author's perceptions, e.g. the mass media is typically a top-down informing process, elected members that have a blog or use Twitter are informing on a more bottom-up basis, the FAGE supports involvement through colloquiums, and so on. With a quick look at this figure one can conclude that the main supported participation processes are informing and consulting, and barely involving. We are thus quite far from our "ideal" participation where all stakeholders could be empowered.

Stakeholders and Processes in Participation

Table 2 is an effort to characterize stakeholders in terms of the typology defined by [START_REF] Colombo | Workflow Modeling for Participatory Policy Design: Lessons form Three European Regions[END_REF]. We have already defined what commissions, groups and circles were in §3.1 and others such as media and citizens are quite self-explanatory. Let us briefly explain some additional stakeholders listed in the table:

• The Presidency is made of four members elected by the Constituent Assembly and it is mainly in charge of applying rules and monitoring the operations of the Assembly (and of signing acts). • The Bureau is formed of one delegate per group of the Constituent Assembly and is mainly responsible for planning, organizing, budgeting external relations. • The General Secretary supports the work of the Constituent Assembly; it comprises legal and administrative workers that are hired by the Assembly. This survey was conducted in an exploratory mode, as the Constituent Assembly was just elected when we began and nothing was in place. Indeed during the first months elected members did not even have an email address and there was no secretary or any support staff. As sessions went along, the Assembly defined its own policies and operational rules. Before we started our survey we had the analysis framework defined in Table 1, and when new requirements appeared we added variables such as levels and approaches of participation, processes and stakeholders.

We believe this context of a Constituent Assembly to be very interesting to investigate participation, as it is rather different from many projects where citizens participate on generic societal issues or very specific topics such as local territorial planning. Indeed, the redaction of a new Constitution really resides at the heart of democratic processes. Our key findings were not so much of a surprise: participation was made mainly through institutional channels with a top-down approach (such as public hearings and thematic consultations); although some interesting bottom-up and mediated channels were set up, most notably the collective proposal. Moreover, eParticipation was rather limited, with a number of elected members using a blog or Twitter to communicate.

As our analysis framework was build in an exploratory manner and suited for a specific context, it needs to be refined and validated in order to be more generic. Along with partners from Switzerland and Germany we are currently preparing a research proposal on participation at the local level and this will be a perfect test-bed to do so.

Fig. 1 .

 1 Fig. 1. Stakeholders positioning regarding participation levels and participation approaches.With a quick look at this figure one can conclude that the main supported participation processes are informing and consulting, and barely involving. We are thus quite far from our "ideal" participation where all stakeholders could be empowered.

Table 1 .

 1 Communication/Coordination Modes and Channels for Participation.

	Communication /	System /	Traditional	eParticipation
	Coordination	Actors	Participation
	mode
			Elections	eVoting
	Institutional	Representative Democracy	formal consultation Consultation: citizen forums, public hearings or any	eVoting
			procedure
		Direct Democracy	Voting, referendums, initiatives	eConsultation
			Articles, opinions,
		Mass Media	interviews, editorials, readers letters, polls,
			phone calls, etc.
			Parliamentary groups
	Mediated	Parties Interest groups Trade unions	Lobbies Strikes Meetings, campaigns, street or door-to-door communication,	Websites, forums, wikis, emails, chats, ePolls, webcasts, social networks, mobile
			tracts, mailings,	communications,
			negotiation	Web 2.0, etc.
			Street or door-to-door
		Citizens	communication,
	Informal	Associations	tracts, free radios,
		Networks	local TVs, cafés,
			clubs, etc.

Table 2 .

 2 Participation at Geneva's Constituent Assembly in terms of Actors and Tasks.

	Stakeholders	Description	Tasks
	Owners	Citizens	Vote and elections
	Decisions-	Elected members	Debate and vote in plenary
	makers		sessions
	Practitioners	Bureau, General	Organize	tasks	of
		Secretary, Presidency,	Constituent Assembly and
		Commissions, Groups,	prepare proposals
		Circles
	Moderators	Media, experts	Comment on Assembly's
			work and provide input
			(e.g. in public hearings)
	Participants	Citizens, associations,	Prepare	petitions	or
		interest groups	collective proposals

Table 3

 3 characterizes the participation channels described in §3.1 regarding the deliberation life-cycle defined by[START_REF] Passas | A Procedural Model for Public Deliberation[END_REF].

Table 3 .

 3 Participation and Deliberative Processes at Geneva's Constituent Assembly.

	Process	Instruments		Description
	Issue	Petitions,	collective	Any person or group can
	emergence	proposals		submit ideas
	Issue	Commissions, Circles,	Commissions	prepare
	structuring	Groups,	Public	proposals under the forms
	Issue analysis	hearings		of articles or general
				principles; groups and
				circles can be heard by the
				Bureau upon request; the
				Assembly can hear any
				representative of the civil
				society, interest groups or
				public authorities.
	Deliberation	Plenary sessions	The Assembly deliberates
				on	proposals	and
				amendments.
	Decision	Plenary sessions	Decision are made by
				majority rule
	Monitoring and	N/A
	Evaluation


Melanie Silvia Wahl
email: melanie.wahl@psycho.uni-tuebingen.de

Margarete Anne Patak

Patrick Pössel

Martin Hautzinger

A school-based universal programme to prevent depression and to build up life skills

Keywords: school-based prevention, depression, adolescents, cognitive-behavioural programme, life skills

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Adolescents and Depression

Depressive disorders in adolescents are a widespread and increasing problem with extensive psychosocial consequences and high costs for society. Epidemiological studies report prevalence rates of up to 20% [START_REF] Birmaher | Childhood and adolescent depression. A review of the past 10 years, part i[END_REF][START_REF] Bettge | Depressive symptoms of children and adolescents in a german representative sample: Results of the bella study[END_REF]. Research indicates that even depressive symptoms that do not reach clinical level in adolescents are strongly associated with an increased risk of developing a depressive disorder and other psychopathologies in adulthood [START_REF] Georgiades | Major depressive disorder in adolescence: The role of subthreshold symptoms[END_REF][START_REF] Birmaher | Childhood and adolescent depression. A review of the past 10 years, part i[END_REF]. Vocational track ("Haupt-und Realschule") students have an increased risk of developing depressive disorders [START_REF] Fend | Depressive Verstimmungen in der Adoleszenz. Verbreitungsgrad und Determinanten in einer Normalpopulation[END_REF]) compared to academic track ("Gymnasium") students. This is consistent with the findings that household income is an important predictor of students depressive symptoms [START_REF] Goodman | The public health impact of socioeconomic status on adolescent depression and obesity[END_REF]) and socioeconomic status is a main predictor for students attending a vocational track school [START_REF] Bos | Schullaufbahnempfehlungen von Lehrkräften für Kinder am Ende der vierten Jahrgangsstufe[END_REF]). Therefore, students from a vocational track of schooling represent a risk population for the development and chronification of depressive disorders while simultaneously these students are hardly reached by usual health care [START_REF] Spoth | Engaging families in longitudinal preventive intervention research. Discrete-time survival analysis of socioeconomic and socialemotional risk factors[END_REF].

The World Health Organization ( 2004) describes prevention as one of the most effective strategies to reduce the burden associated with psychological diseases. Prevention can be divided into three types: universal, selected, and indicated programmes. Indicated interventions target children and adolescents identified with mild to moderate symptoms of a disorder, selective strategies involve individuals identified as at risk of psychological problems while universal prevention programmes can be applied to a whole population regardless of their risk status or the development of early psychological symptoms. Therefore universal prevention programmes can be delivered in a regular school curriculum and are associated with other advantages, including the loss of screening problems, recruitment attrition difficulties and stigmatization [START_REF] Offord | Selection levels of prevention[END_REF]. Empirical support for preventive strategies conducted in school settings is growing. Especially encouraging are results showing no difference in effectiveness of a universal prevention programme between students from different socioeconomic groups [START_REF] Junge | A randomized placebo-controlled trial of a school-based depression prevention program[END_REF]). For the prevention of depression there are some evaluated universal programmes available (e.g. the Resourceful Adolescent Programme -RAP, [START_REF] Shochet | The efficacy of a universal school-based program to prevent adolescent depression[END_REF]; or the Problem Solving for Life Programme -PSFL, [START_REF] Spence | Preventing adolescent depression: An evaluation of the problem solving for life program[END_REF]. There are also evaluated programmes which target both anxiety and depression (e.g. Gesundheit und Optimismus -GO!, [START_REF] Junge | A randomized placebo-controlled trial of a school-based depression prevention program[END_REF]or FRIENDS, Barrett et al. 2000). We describe in this article our own, successfully evaluated, universal programme to prevent the development of depressive symptoms in adolescents (LARS&LISA; Pössel et al. 2004b).

Theoretical background

On what universal prevention programmes should focus

The social information-processing model of social competence developed by [START_REF] Dodge | Social-cognitive mechanisms in the development of conduct disorder and depression[END_REF] represents a convincing rational for (universal) prevention programmes and psychological interventions.

According to this model behaviour in reaction to situational stimuli is the consequence of an information-processing sequence with five stages: encoding, mental representation, response accessing, response evaluation and selection and enactment. In the encoding stage selective perception filters relevant aspects of the stimulus, which are then stored in short-term memory. Stimuli are stored in memory primarily according to their significance for the individual ("mental representation").

Significance is connected with the individual's emotional needs and intentions. On this basis emotional and behavioural reactions are generated in the stage of response accessing. Various mental representations are connected with different reactions like physical activity, verbalisation, endocrine secretion, arousal of the autonomous systems and affect based on rules of the associative network of the brain (for example "cry when rejected"). In the response evaluation and selection the individual evaluates the prepared reactions and decides on one of the alternatives, depending on moral, acceptability and/or anticipated consequences. If the criteria are not met, the process of decision is repeated until a suitable reaction can be chosen. If this evaluation succeeds, the decision can be initiated on in the final stage (enactment), which can take on the form of verbalisation, physical activity, autonomous activity, and other reactions, which can be seen or measured.

Dysfunctional interpretations of situational stimuli, emotional reactions, and withdrawal from social situations are a result of malfunction of one or more information-processing stages. Different studies could show that the information-processing of children with increased depressive symptoms is deficient, for example they showed more negative and less positive social information processing [START_REF] Bell | The children's evaluation of everyday social encounters questionnaire: Comprehensive assessment of children's social information processing and its relation to internalizing problems[END_REF]. Depressive information processing predicted the level of depression of adolescents a year later (Pössel et al. 2006b). Prevention programmes, therefore, should focus on the biased information processing as a modifiable feature and show the relationship to emotional responses and social behaviour. In a further step assertive behaviour as well as social competence skills should be trained to provide for alternative responses.

To gather momentum for change it is expedient to focus on as many levels of social information processing as possible. For the encoding stage self-observation and situational analyses help to become aware of functional relationship and improve perceptual skills. On the mental representation stage social stimuli are interpreted. In order to motivate for change, it is necessary to develop an understanding of the key concepts through exploring the relationship between cognition, emotion, and behaviour (LARS&LISA programme element "Reversible Spiral"). Social stimuli attributions are targeted by identifying dysfunctional, automatic thoughts and by replacing them with more functional thoughts (LARS&LISA programme element "Think"). To establish functional response alternatives on the response accessing and response evaluation and selection stage youths can experience various functional response alternatives and their positive consequences in a skill and competence training (LARS&LISA programme element "Just do it") and a social approach (LARS&LISA programme element "Get in touch"). These modules build up self-confidence and provide behaviour which is incompatible with social withdrawal. The most important element of the social part of a training programme is role-play because it simultaneously allows modification on both stages of information processing. Adolescents can experiment with new and more functional response alternatives and observe and experience their own and others' reactions to these. At the same time, as a further step, the translation of the new response options into actual behaviour is accomplished In addition to these basic elements that need to be accomplished by effective prevention programmes, a motivational component for active participation is necessary (LARS&LISA programme element "Set your goals"). Participants should develop their personal goals, work out how to formulate them and what they can do to reach their goals step by step, starting from now on. Goals should be specific, measurable, and divisible into sub-goals. It is important that the progress of achievement is frequently observed. It is advisable for group members to report their personal gains at the end of each session considering why and how the programme contents are helpful in reaching that personal goal.

Adolescents experience that they themselves can be active in influencing their life and well-being and develop a sense of self-efficacy. A successful universal prevention programme should include the main components presented in figure 1.

Please insert figure 1 here.

Method

The universal prevention programme of depression: LARS&LISA In German two different versions of the prevention programme exist, one version for groups of higher vocational track ("Realschule") students (LARS&LISA; Pössel et al. 2004b) and one for lower vocational track ("Hauptschule") students (Lebenslust mit LARS&LISA1 ). In addition, there exists a version for high-school students in the USA. All three versions are identical in regard to the contents of the sessions, they vary in a different degree of complexity of language and difficulty of tasks according to the academic level of the target population and the layout and pictures of the peer coping models, which are ethnically matched to the audience. In the following the basic programme contents of the three versions are described more precisely.

LARS&LISA is delivered to students 8 th grade or higher (≥13 years of age) once a week over a 10week period during regular school hours. One session is composed of two 45-minute class periods for a total of 1.5-hours per session. The programme is administered to students in same-gender groups 6 because adolescents may be hesitant to portray themselves authentically in front of peers of the other sex. In fact, for boys, research has shown that sex-homogeneous groups can create contexts in which boys can share feelings and emotions without embarrassment [START_REF] Sukhnandan | An investigation into gender differences and achievement: Phase 2. School and classroom strategies[END_REF], be less distracted, and be more open and responsive without fear of compromising their "laddish" image in front of girls [START_REF] Warrington | we decided to give it a twirl': Single-sex teaching in english comprehensive schools[END_REF]. Furthermore, a pilot study has shown that adolescents in this age group work more effectively and openly as a team if they participate in same-sex groups [START_REF] Pössel | Erste Ergebnisse eines Programms zur schulbasierten Prävention von depressiven Symptomen bei Jugendlichen[END_REF]. LARS&LISA can be led by one or two group leaders and by psychologists or teachers with equal outcome in same-gender groups. Groups with 8 to 12 students are ideal but the programme also works with smaller (4) and bigger (up to 20) groups (Pössel et al. 2004b). Following table 1 shows the elements of each session.

Please insert table 1 here.

For facilitation of programme elements, educational film models are available with adolescents functioning as actors in different situations (German programme versions only). Subjective of these film trailers is to enlarge knowledge about social situations and adaptive social behaviour, to enhance motivation for change by identification with peers (Lars and Lisa) acting as coping models. Students learn that their well-being and behaviour is not necessarily predetermined by an external situation but instead by their own cognitions and behaviour which can be influenced. The films are also a tool to loosen up the lesson in order to keep up concentration and active participation.

Main session contents

The group leader manual describes the content of every session in a self-explanatory way and in great detail. Each session follows the same structure, which is summarized on the first page (including material list, session goals and suggested timing) for each training session. The sessions begin by making a semi-circle, review the knowledge quiz, which was filled in at the end of the previous session, and a presentation of the topic of each session. Then the specific session content is worked on, to be concluded with making connections and the feedback at the end of the session. In the following these elements will be specified.

Before the start of the session, we recommend to form a semi-circle with the group members in order to create a more open atmosphere and encourage teamwork. Another advantage of the semi-circle is that it helps to distinguish from the regular school curriculum seating plan. Some classes may require a usual seating plan in order to keep up a work-oriented atmosphere (especially boys' groups). Another method used in every session is the knowledge quiz, which is handed out at the end of each session.

The group leader corrects the quiz by the next session and gives positive feedback (positive reinforcement) to the whole group at the beginning of the following session. Main purpose of the knowledge quiz is to check whether the material of the session has been understood by the students and if not, to review corresponding contents. The topic of each session as well as session goals are presented as an overview before the actual session programme starts, in order to arouse participants´ interests and to connect former and following lessons. Making connections is the most important content of each session. Students are invited to find examples how they can use what they have learned this session in their every-day life and how they can reach their goals this way. It shows students how LARS&LISA can influence their everyday lives and aims to encourage members to use the strategies and to maintain motivation for active participation. At the end of each session, group leaders are advised to give feedback to the group regarding their observance of the group guidelines.

Feedback should always be positive and reinforcing using specific examples without activating reactance that may result from negative feedback.

Suggested coaching style and group leader qualifications

In the manual we give suggestions regarding group leader behaviour which results out of years of work with the LARS&LISA programme. A group leader needs to have knowledge of cognitive and learning theory and should have knowledge of social skills training and experience in working with adolescents. We recommend attending a training workshop for group leaders. Basic guidelines for coaching style include: goal transparency, ensure understanding of theoretical background, differentiation of knowledge-related and personal questions, handling of wrong students answers, positive reinforcement and handling of difficult group interaction. In addition, one chapter of the group leader manual includes a summary of pedagogical background: prevention of disturbance by activation, lesson flow, precise guidelines and presence and stop signals. To help to get started, several "Ice Breakers" (Get to know games) are described in the manual. Every session also includes a game with regards to the content of the session.

What do the LARS&LISA sessions look like?

To illustrate the approach of LARS&LISA two sessions shall be described in detail. In session 3 ("Reversible Spiral -I") the students practise identifying thoughts and feelings. They learn how thoughts, feelings and behaviour are associated. The concept of dysfunctional and functional thoughts (called "down-thoughts" and "up-thoughts") are introduced.

Differentiation between thoughts, feelings, and behaviour is developed with the help of examples and elaborated through several exercises like an emotion charade, in which the adolescents pantomime an emotion and the group postulates which situation, thoughts, and actions could accompany these. The aim of this session is that it becomes apparent to the adolescents that the same situation can lead to very different feelings depending on the interpretation of the situation, and by this mechanism the adolescents can influence their own emotional condition.

In the worksheet shown below (figure 2) the relationship between cognitive, emotional, and behavioural variables is illustrated by a reversible spiral. With an example drawn from the life of the adolescents like the following adolescents can understand the impact of functional and dysfunctional thoughts: A student receiving a bad grade in a maths-test can either react with "down-thoughts" ("I am too stupid, I will never understand maths"), feelings of despair and depression and avoidance of maths with the result of further deterioration in that subject, or with "up-thoughts" ("I was not prepared well enough for this test, I will ask my friend to explain the subject to me and start learning for the next test"), which lead to better coping and confidence with functional behaviour like increased effort. In the following sessions the concept of the "reversible spiral" is recessed with multiple methods, for example a film story where Lars has "down-thoughts" which block the progress toward a goal. The conclusion is drawn that situations can not always be changed, but the way we think about situations can be controlled.

In the knowledge quiz the students are invited to apply this scheme to a situation of their own life.

Like each session this one ends with the embedding of the new issues in the daily life of the adolescents by making connections with different situations, how they can transfer the achieved knowledge into practice, and by referring to the example from the film model.

Please insert figure 2 here.

In session 7, our second example for a specific session, the emphasis is on social skills. First, several social situations described by the students as potentially difficult for some people (not necessarily difficult for themselves) are collected. With the help of three scenes from a film, in which a youth acts in the same situation in an either passive, aggressive, or assertive way, students develop an understanding of how to identify different types of behaviour based on voice, eye contact, body language, and speech content (figure 3).

Please insert figure 3 here.

Then the pros and cons of the different types of behaviour are discussed with a focus on the short-term pros of aggressive and passive behaviour, which the students experience in their every-day situations.

At the same time, the discussion focuses on the long-term disadvantages of aggressive and passive behaviours. The aim of this strategy to discuss aggressive and passive behaviours is that adolescents realize that only assertive behaviour leads to positive long-term consequences by respecting yourself as much as the counterpart. This session creates the basis in knowledge, skills, and motivation for the practical implementation through role-play in the following sessions.

Results

The school based universal prevention programme LARS&LISA is extensively evaluated by three studies with German adolescents and one study with American high-school students. LARS&LISA has shown positive effects on the participants` social network [START_REF] Pössel | Erste Ergebnisse eines Programms zur schulbasierten Prävention von depressiven Symptomen bei Jugendlichen[END_REF], symptoms of depression [START_REF] Pössel | Influence of general selfefficacy on the effects of a school-based universal primary prevention program of depressive symptoms in adolescents: A randomized and controlled follow-up study[END_REF]Pössel et al. 2004a;[START_REF] Pössel | Impact of comorbidity in prevention of adolescent depressive symptoms[END_REF], and aggressive behaviour [START_REF] Groen | Universelle, schulbasierte Prävention der Depression im Jugendalter: Ergebnisse einer Follow-Up-Studie[END_REF]), compared to teaching as usual as well as compared to a prevention programme based on Pennebaker's paradigm of expressive writing (Pössel et al. 2006a). In addition, the LARS&LISA programme for lower vocational track students is in the process of evaluation, showing first promising results regarding the implementation of the programme through teachers as group leaders.

Outlook

The LARS&LISA evaluation study with lower vocational track students currently demonstrates high response rates (more than 85% so far) at 12-month follow-up assessment. The projected 18-month follow-up is expected to be difficult as the participating students will by then have already left school and therefore be hard to reach.

Besides the acquisition of knowledge, education and advancement of cognitive skills, school should also address social learning and social skills for every day life, to empower resources and resilience, and to train coping strategies. By this, school can strengthen positive attitudes towards life, enhance emotion regulation and self-efficacy and consequently, foster health and the prevention of health problems. With LARS&LISA an attractive and well evaluated universal prevention programme is available in different versions for the specific needs of 8 th grade boys and girls (13-14 years) in diverse school settings. It can be easily implemented, is highly manualised, and provides didactic materials and exercises which allow the possibility of teachers to be group leaders.

Fig. 1 :

 1 Fig. 1: The main elements of a universal prevention programme (adapted from Pössel et al. 2004b)

Table 1 : Contents of the LARS&LISA prevention programme 14

 1

Table 1 :

 1 Contents of the LARS&LISA prevention programme Discussion about the associations among feelings, thoughts and behaviour (the "reversible spiral") -Introduction of the concepts "down-thoughts" (self-critical, negative thoughts) and "up-thoughts" (self-supportive, positive and realistic thoughts) 4 Reversible Spiral II -Identifying the signs of a down-thought -Exploring the meaning of a down-thought 5 THINK I -Introduction and training of the "reality check" -Creating one's own up-thoughts 6 THINK II -Review meaning of up-thoughts -Clarify the need for up-thought training -Identification of daily situations into which up-thoughts may be integrated 7 Just do it I -Identification of the signs of assertive, passive, and aggressive behaviour -Discussion of the pros and cons of different behaviour styles -Exploration of the association among down-thoughts, upthoughts and assertive, passive and aggressive types of behaviour 8 Just do it II -Demonstration that assertive behaviour can be learned -Role-play to exercise assertive behaviour 9 Get in touch I -Demonstration of verbal and nonverbal strategies to signal interest in others -Role-play to practice "get in touch" 10 Get in touch II -Role-play to practice "get in touch" -Review and evaluation of the main LARS&LISA components -Group members evaluate training and indicate what they would change -Wrap-up

	Session Title/Main Topic	Content
	1	Get started	-Introduction
			-Presentation of the group guidelines: fairness, support, and
			teamwork
			-Consequences of guideline-breaking
			-Explanation of the goals and main components of
			LARS&LISA
	2	Set your goals	-Defining personal goals
			-Formulation and operationalisation of personal goals
	3	Reversible Spiral I	-Defining "feelings", "thoughts" and "behaviour"
			-

Available at http://www.uni-tuebingen.de/psychologie/abtkpps/lars_und_lisa/materialien.htm

Acknowledgments

Research supported by German Research Foundation (DFG) and Federal Ministry of Education and Research (BMBF).

Conflict of interests

The authors declare that they have no conflict of interest.