Elena Viorica Epure
email: elena.epure@malix.univ-paris1.fr

Slavko Zitnik
email: slavko.zitnik@fri.uni-lj.si

Dario Compagno
email: dario.compagno@univ-paris3.fr

Rebecca Deneckere

Camille Salinesi
email: camille.salinesi@univ-paris1.fr

Camille Salinesi Automatic

Automatic analysis of online conversations as processes

The tremendous use of social media has changed the way society communicates and interacts nowadays, leading to a plethora of online conversations [START_REF] Perrin | Social media update 2016[END_REF]. The increasing availability of these conversations as behavioral traces has enabled automatic approaches for behavior discovery and analysis. These approaches, grounded in machine learning, data mining and language processing have become effective predictive components and intelligent descriptive tools for many domains. In robotics, online conversations have been used for training dialogue bots [START_REF] Wu | Speech act modeling in a spoken dialog system using a fuzzy fragment-class markov model[END_REF] ; in politics to analyze communication mechanisms between disseminators and public [START_REF] Hemphill | Tweet acts : How constituents lobby congress via twitter[END_REF] ; in security, to enable modeling of narratives and the prediction of their influence on the crowd behavior [START_REF] Houghton | Modeling the influence of narratives on collective behavior case study : Using social media to predict the outbreak of violence in the 2011 london riots[END_REF].

A widespread method to analyze automatically conversations emerges from pragmatics, specifically from speech act theory, which sustains that human communication is driven by intentions [START_REF] Searle | Speech acts[END_REF]. Conversation analysis research [START_REF] Searle | Searle on conversation[END_REF]) considers these intentions possible adequate concepts for representing conversations and inferring behavioral knowledge. This view has been also adopted by computer science community and subsequently exploited in automatic analyses. In general, such solutions rely on three steps : adopt an existing intention taxonomy or define a new one ; use or create a tagged corpus ; build the automatic technique either by defining relevant features for machine learning or by creating new algorithms based on text and language processing, and evaluate it on the tagged corpus.

Even though existing works brought significant contributions, there are several limitations and open issues to be tackled. First, the proposed intention taxonomies in linguistics are either too general [START_REF] Searle | Speech acts[END_REF] or too detailed [START_REF] Vanderveken | Meaning and speech acts[END_REF] to enable facile manual classification by non-experts. Further, the proposed intention taxonomies in computer science are often specialized for their target goals or corpora [START_REF] Bhatia | Identifying the role of individual user messages in an online discussion and its use in thread retrieval[END_REF][START_REF] Stolcke | Dialogue act modeling for automatic tagging and recognition of conversational speech[END_REF], making it challenging to reproduce on other types of online conversations. Second, conversation corpora created for enabling automatic intention identification are tagged per dialogue turn. However, turns of multiple sentences as often appear on social media has seldom a unique intention [START_REF] Bhatia | Identifying the role of individual user messages in an online discussion and its use in thread retrieval[END_REF]. Third, there is scarce computer science research on modeling conversations as processes though such view exists already in linguistics [START_REF] Searle | Searle on conversation[END_REF]. The process mining community proposes automatic methods and techniques to discover processes and to analyze them interactively by relying on relevant and well formed logs of traced behavior [START_REF] Aalst | Process mining[END_REF]. However, process mining has been rarely applied to text and the existing methods are unsuitable for our goal [START_REF] Osman | From natural language text to visual models : A survey of issues and approaches[END_REF].

Our aim is to create a general approach for analyzing automatically online conversations in order to reveal behavior as processes and to enable research in the affiliated domains. For that, we decided to apply standard process mining techniques on logs we design to capture relevant conversation behavior. The units of such logs are intentions and our next focus is to ensure these logs are well-formed and capable to reveal reliable and correct processes. So far, we aimed to improve the limitations of automatic intention discovery through : 1) a general intention taxonomy grounded in linguistic and empirical analysis, evaluated for reproducibility and facile manual tagging with non-experts ; 2) a Reddit corpus with dialogue turns of multiple utterances, tagged per utterance that complements existing corpora (2280 utterances) ; 3) an evaluation of multiple classification algorithms and of features' importance for intention discovery ; domain-independent, discourse features are proposed apart from classical content features ; weighted macro f-scores up to 78% are obtained in a 10-fold cross validation setup.