The October 2016 constitutional referendum in Côte d'Ivoire

Christian Bouquet

Christian Bouquet is emeritus professor of political geography at Bordeaux Montaigne University and researcher in CNRS's Africas in the World research unit (Afriques dans le monde, LAM).

Immediately after his re-election as president of Côte d'Ivoire on 25 October 2015, Alassane Ouattara confirmed that he wished to carry out a major reform to the constitution, which would be put to a referendum. This came as no surprise, as the preceding constitution rapidly adopted on General Gueï's initiative in July 2000 contained article 35, the notorious article which had played a significant part in unleashing the rebellion in 2002, as it stipulated that only candidates whose mother and father were both of Ivorian origin could stand for election as president. It therefore excluded de facto Alassane Ouattara's bid, and it took international mediation carried out by South African politician Thabo Mbeki in the April 2005 Pretoria Agreement to get around the regulation to allow Ouattara, as the leader of the main opposition party, to contest Laurent Gbagbo in the 2010 election. As the article was still included in the constitution, but was in contravention of a number of international agreements to which Côte d'Ivoire was signatory, it was necessary to change it, especially as there were no longer any politicians who wished to see it retained. Fears about the reforms to the constitution revolved around the limits to the number of mandates.

Although the re-elected president had always insisted that he would keep to two mandates, some in the opposition suspected the president of having ulterior motives. This turned out not to be the case,1 so the opposition focused its criticisms on two elements in the reform which they believed to be highly debatable: the creation of the post of vicepresident and the establishment of a senate. Alassane Ouattara had frequently stated his intention to create the post of vicepresident, as he feared that any sudden and brutal interruption to his mandate could lead to a crisis similar to that which followed the death of Houphouët-Boigny. He had also taken a keen interest in the Nigerian and Ghanaian systems which tended to protect the regime from this risk.

As for the senate, he was adopting an idea proposed by Laurent Gbagbo-making nonsense of the opposition's stand-although it is true that his plans for the composition of the second chamber included a third of seats designated by the president (thirty-three) and sixty-six elected by local communities. The new constitution also contained measures which could have major consequences, such as a national chamber for kings and traditional chiefs (which could be seen as a politicisation of traditional chiefdoms) and the recognition of individual private property (which maintained ambiguity with regard to rural property ownership). Ouattara's opposition, however, seemed uninterested in these issues.

Gbagbo's Front populaire ivoirien (Ivorian Popular Front, FPI) and the small parties which had supported Gbagbo did not appeal to its voters to vote against the constitution but rather to boycott the vote, as did some Parti démocratique de Côte d'Ivoire (Democratic Party of Côte d'Ivoire, PDCI) dissidents who accused those in power of acting precipitately.

The vote on 30 October 2016 used the same electoral roll as the 2015 presidential elections (6,313,758 registered voters) and went off without incident. There were 2,500,000 fewer voters than inhabitants recorded in the 2014 population census, but Côte d'Ivoire seems to have become accustomed to this chronic lack of registered voters. Some commentators criticized the fact that there wasn't just one ballot paper, but a Yes paper and a No paper, which made votebuying much easier.

Abstention was high, with participation (see Map 1) at 42.2% (compared to 55.05% in 2000), ranging from 99.5% in the department of Séguélon down to 10.08% in Dabou. Moreover, observers expressed their concerns about areas where high participation was recorded, particularly in the North, traditionally pro-Ouattara, but also in the South, traditionally unfavourable to Ouattara. These reservations were noted in the (now classic) accusations of 'widespread fraud' levelled by the opposition over the results (see Map 2). There was resounding victory for the Yes vote (93.42%), particularly in the pro-Ouattara regions in the North, which was unsurprising as no-one had encouraged voters to vote No. In fact, the Yes vote was also proportionally higher in the regions where Ouattara was not traditionally supported. Nevertheless, no appeal was registered and no complaints were made by the observers, even if in most polling stations the only representatives were those belonging to the ruling party. Thus the new constitution took effect, creating the de facto third Republic, whose democratic legitimacy is now unquestioned.

Although the change to the constitution would allow the outgoing president to stand again in the first mandate of the new republic.

referendum in Côte d'Ivoire

Participation by department


Eloïse Berson
email: eloise.berson@gmail.com

Ngoc Q K Duong

Claire-Hélène Demarty
email: claire-helene.demarty@technicolor.com

Collecting, Analyzing and Predicting Socially-Driven Image Interestingness

Keywords: Image interestingness, content and social interestingness, Flickr, LaFin dataset, contextual information, deep learning

Interestingness has recently become an emerging concept for visual content assessment. However, understanding and predicting image interestingness remains challenging as its judgment is highly subjective and usually context-dependent. In addition, existing datasets are quite small for in-depth analysis. To push forward research in this topic, a large-scale interestingness dataset (images and their associated metadata) is described in this paper and released for public use. We then propose computational models based on deep learning to predict image interestingness. We show that exploiting relevant contextual information derived from social metadata could greatly improve the prediction results. Finally we discuss some key findings and potential research directions for this emerging topic.

I. INTRODUCTION

There has been a recent surge of interest in understanding and predicting user perceptions on visual content. Such perceptions have moved toward challenging high level subjective concepts such as emotion [START_REF] Rimmele | Emotion enhances the subjective feeling of remembering, despite lower accuracy for contextual details[END_REF], [START_REF] Soleymani | Affective characterization of movie scenes based on content analysis and physiological changes[END_REF], popularity [START_REF] Khosla | What makes an image popular?[END_REF], [START_REF] Cappallo | Latent factors of visual popularity prediction[END_REF], memorability [START_REF] Isola | What makes an image memorable?[END_REF]- [START_REF] Cohendet | Mediaeval 2018 predicting media memorability task[END_REF], and interestingness [START_REF] Gygli | The interestingness of images[END_REF]- [START_REF] Constantin | Computational understanding of visual interestingness beyond semantics: literature survey and analysis of covariates[END_REF]. One reason for this surge is the exponential increase of sharing of images and videos, that raises an essential need for filtering the content in each large-scale retrieval and recommendation system. Such a filtering process is impossible without a clear understanding of the content's subjective meaning. Focusing solely on interestingness and how contextual information may help its prediction, this paper presents our contributions to this emerging topic in multimedia assessment.

Interestingness usually refers to arousing interest, curiosity, as well as the ability of holding or catching attention [START_REF] Berlyne | Conflict, arousal and curiosity[END_REF]. This important notion has been intensively investigated for years in psychology and vision research as it involves human perception [START_REF] Chen | An examination of situational interest and its sources[END_REF], [START_REF] Elazary | Interesting objects are visually salient[END_REF]. These studies revealed that interest is determined by certain factors like novelty, uncertainty, conflict, complexity, and their combinations. This finding was also supported in the appraisal theory presented in [START_REF] Silvia | What is interesting? exploring the appraisal structure of interest[END_REF], where the author explained that appraisals like the novelty, the comprehensibility, and the complexity of an event are likely to arouse interest in this event.

Following the existing literature, we distinguish two different notions, namely socially-driven interestingness and

The work was done while the authors were with Technicolor.

content-driven interestingness. The latter refers to human annotations that assess interestingness solely on the perceived content. Content-driven interestingness has been extensively addressed in the MediaEval benchmark on Predicting Media Interestingness1 [START_REF] Demarty | Mediaeval 2017 predicting media interestingness task[END_REF]. In this paper, we focus on socially-driven interestingness for which the definition is derived from media sharing websites such as Flickr and Pinterest where posted images are assigned with interestingness labels. Such labels are usually inferred based on social aspects such as number of views, tags, comments, user reputations and viewer's profiles2 but also on the characteristics of the content itself. In that sense, socially-driven interestingness differs from popularity which solely corresponds to the number of likes, favorites, reshares or views attached to a given content3 [START_REF] Khosla | What makes an image popular?[END_REF], [START_REF] Cappallo | Latent factors of visual popularity prediction[END_REF], even though the two notions might be linked. The assumption that socially-driven interestingness also derives from the content itself was in part proven in [START_REF] Dhar | High level describable attributes for predicting aesthetics and interestingness[END_REF] where the authors achieved good performance for the prediction of Flickr interestingness scores with high level aesthetics attributes extracted from the content as input to an SVM classifier.

Moving toward computational aspects, visual interestingness have recently been explored in multimedia and computer vision communities [START_REF] Demarty | Predicting interestingness of visual content[END_REF], [START_REF] Amengual | Review of Methods to Predict Social Image Interestingness and Memorability[END_REF]- [START_REF] Grabner | Visual interestingness in image sequences[END_REF]. As an example, Gygli et al. [START_REF] Gygli | The interestingness of images[END_REF] investigated the use of various features (RGB values, GIST, spatial pyramids of SIFT histograms, colorfulness, complexity, contrast and edge distributions, arousal and composition of parts) that computationally capture unusualness, aesthetics, and general preferences. These features were used to train a Support Vector Regression (SVR) model for interestingness prediction. Authors in [START_REF] Hsieh | Investigating and predicting social and visual image interestingness on social media by crowdsourcing[END_REF] investigated the correlation of color, texture, edge and saliency features with both content-driven and socially-driven interestingness. Recently, deep learning has also been investigated for the task, but with small available datasets, its power was limited [START_REF] Shen | Technicolor@MediaEval 2016 Predicting Media Interestingness Task[END_REF]. Readers are referred to [START_REF] Demarty | Predicting interestingness of visual content[END_REF], [START_REF] Constantin | Computational understanding of visual interestingness beyond semantics: literature survey and analysis of covariates[END_REF] for a more comprehensive survey on the existing work.

In the literature, some work also investigated the use of context for the prediction of interestingness. Chu et al. [START_REF] Chu | The effect of familiarity on perceived interestingness of images[END_REF] investigated the effect of familiarity (facial familiarity and familiarity with image context) in the perceived interestingness of images. Liu et al. [START_REF] Liu | Estimating interestingness of images based on viewer data[END_REF] built a computational model based on viewer data and viewer's profiles to estimate the interestingness of images. Rajani et al. [START_REF] Rajani | Identifying interestingness in fashion e-commerce using pinterest data[END_REF] predicted interestingness of fashion products for online shopping, where they assumed that all pins related to fashion on Pinterest are interesting [START_REF] Rajani | Identifying interestingness in fashion e-commerce using pinterest data[END_REF]. The authors used Word2Vec [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] to transform textual data from pins to machine learning features. Closer to our work, although the authors are targeting popularity, Gelli et al. [START_REF] Gelli | Image popularity prediction in social media using sentiment and context features[END_REF] tried to use visual sentiments conveyed by the images together with contextual features extracted from tags, descriptions and titles of Flickr images, to predict popularity of images.

This paper states several contributions to the emerging topic of image interestingness as follows. In Section II we first describe a large-scale socially-driven interestingness dataset, which consists of more than 123k Flickr images together with their metadata and interestingness labels. We then present some insight on the associated metadata, that may help for the understanding of the socially-driven interestingness concept. The dataset will be made publicly available for the community so as to push forward research in the domain 4 . In Section III, we propose computational models to predict interestingness of images. From the obtained results, we highlight the importance of contextual information and discuss some key findings in Section IV.

II. LAFIN: LARGE-SCALE FLICKR INTERESTINGNESS

DATASET

Apart from some very recent dataset 5 , no large-scale socially-driven interestingness dataset with additional contextual information was existing up to now. Some datasets exist that come from Flickr's images but either they are not publicly released [START_REF] Dhar | High level describable attributes for predicting aesthetics and interestingness[END_REF], or they are not associated with interestingness scores 6 , or they contain only images with positive interestingness labels, and no negative samples [START_REF] Huiskes | New trends and ideas in visual concept detection: The mir flickr retrieval evaluation initiative[END_REF]. This motivated the construction of the LaFin dataset, which contains binary interestingness label for images. Note that defining a relevant human annotation protocol to collect such social labels is far from being easy due to the subjectivity of the task, especially when one wants to build annotations at large. Thus, we rely on available Flickr interestingness labels for our dataset. We believe that this initiative is already providing valuable information and can be considered as a first step toward building a new dataset with human labels in the future.

A. Image dataset collection

The LaFin dataset consists of more than 123k images equally balanced between interesting and non interesting samples and collected by following a two-step process: 1/ a first set of 200k images was collected from Flickr7 , 2/ followed by a filtering of the images based on their associated metadata.

A first set of 100k interesting images was gathered through Flickr Interestingness API 8 , at a rate of 500 images per day (Flickr maximal limit) for several months. When ever possible, each image was downloaded at the highest available resolution. No clear description of the algorithm used to compute Flickr interestingness labels is available but refering to [START_REF] Butterfield | Interestingness ranking of media objects[END_REF], we may infer that Flickr interestingness label is related to at least the number of views, the number of comments and who comments on a specific picture, tags applied to the picture, Flickr discussion groups in which the picture appears, favorites, a.k.a Flickr bookmarking, of the picture, existing relationship between the owner and a viewer of the picture and time varying behavior of the above factors. From this list of probable factors used in the computation of interestingness labels, it appears clearly that what Flickr provides is an estimation of a sociallydriven interestingness level, more than of a content-driven interestingness level, as defined in Section I. It should also be noted that most of the images returned by Flickr API are owned by semi professional or professional photographers, leading to the conclusion that the subset of interesting images might be biased towards aesthetics images.

In parallel, 100k additional Flickr images were collected from the same days, to build a subset of non interesting images, by following the simple rule that one image is non interesting as long as it does not belong to the list of interesting images provided by Flickr. Note that we processed so because Flickr is not providing any API to collect non interesting images and it does not also provide any interestingness scores with its interesting images. With the assumption that Flickr will provide the first 500 most interesting images per day through its API, we can only assume that these non interesting samples are not ranked among the first 500 interesting images.

B. Contextual information from the metadata

Together with the images, classified into interesting/non interesting, we also collected all additional socially-driven metadata available on Flickr's web site in an attempt to provide the associated contextual information that might influence the appraisal of users. Such metadata include title, description, associated tags, owners information, license, upload date, comments, number of views, original format, location, etc. Although some metadata are equally present in both classes, e.g., locations, tags, titles, views, others have an unbalanced distribution over the two classes, e.g., comments and descriptions. Indeed a single image in the interesting class may have several comments and tens of views attached to it, whereas most of non interesting images will have no comments, nor views. Indeed, interesting images got 97.2% of the total number of views.

With the aim of further modeling contextual features, we proceeded to a qualitative study of descriptions, titles and tags attached to LaFin images. Descriptions, as it could have been expected, often only correspond to a plain description of the visual content. It does not really bring new information regarding context, nor does it really directly relate with the social aspect of interestingness. This is also true for titles for a non negligible part of the images, although they might also contain other additional information than pure description (e.g., May be next time, Deep thoughts). As for tags, although some of them also correspond to descriptive attributes of the images (sunset, street), they seem to bring other information relative to context such as the location (Japan, London), the quality and type of the image (canon, black and white), its topic (art, portrait, architecture), etc. We believe that this additional contextual information, different from a pure description of the image content might help in the task of predicting the interestingness of content. Thus in the following section, we further filter our dataset thanks to the tags and we use them in Section III as input to our computational models.

C. Filtering and additional features

With the target of keeping the largest possible number of images from the initial set of 200k images, while retaining the balance between the two classes interesting/non interesting in terms of contextual information, we further filtered the original image set to keep only images with at least one tag. Although, from the above study, titles also seem to bring contextual information, filtering on tags was maximizing the size of LaFin dataset. LaFin dataset finally contains roughly 123k images, equally spread between interesting and non interesting samples. Additionally to the images, their binary labels, and associated metadata, some precomputed features are provided: CNNs, semantic features that derived from image captioning and Word2Vec representations of Flickr tags. See Section III-A for a complete description of these features.

III. IMAGE INTERESTINGNESS PREDICTION

The global workflow of our investigated computational models for image interestingness prediction is depicted in Figure 1. The system takes as input images and their associated tags and outputs the corresponding interestingness labels. As far as the modeling is concerned, we targeted to: 1) Find what different features bring to the prediction performance.

2) Study the influence of context information coming from the tags on the prediction. 3) Prove that social interestingness is also related to the content itself as content-based features help improving the prediction when compared to tag features alone. 4) Finally we would like to note that, as far as we know, high level semantic image captioning features are used for the first time for the task. For each model, the LaFin dataset was split into 64% for training, 16% for validation, and 20% for testing. Details of each step are given below.

A. Feature extraction CNN features:

As CNN provides a powerful feature representation useful for many different tasks, we used the wellknown VGG16 network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] pre-trained on the ImageNet dataset for the extraction of a first image feature. The feature is extracted from the last fully-connected layer before the softmax and has a dimension of 4096.

Image captioning-based features (IC): As scene semantics and high-level visual attributes (such as emotions, actions, movements, appearance of objects, etc.) may also derive human interest in an image, we further investigated the use of semantic features as computed by an image captioning (IC) system [START_REF] Kiros | Unifying visual-semantic embeddings with multimodal neural language models[END_REF]. Such an IC model builds an encoder comprising a CNN and a long short-term memory recurrent network (LSTM) for learning a joint image-text embedding. The CNN image feature and the Word2Vec representation [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] of the image caption are projected on a 2D embedding space which enforces the alignment between an image and its corresponding semantic caption. We extracted the projected CNN feature for each image, as logically it should contain some semantic information expected to be helpful for the prediction of image interestingness. This feature has a dimension of 1024.

Word2Vec features from Flickr tags: With the target of investigating the use of contextual information, we used textual information coming from the tags associated to LaFin images. We are especially interested in discovering how IC features compare with those contextual features, as both convey semantic information. We extracted a 300-dimensional word embedding vector [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] for each tag associated with one image. Those Word2Vec features are then averaged to obtain a single feature vector per image. Note that we also filtered all tags containing the string explor as potentially referring to Explore 9 .

Word2Vec features from IC-based generated tags: For each image and its projection in the IC embedding space, we extract the 10 nearest neighbors. These neighbors in the feature space allow us to artificially build 10 tags related to the input image, called generated tags. As the COCO10 dataset used in the training of the IC system is large enough, we were able to get relevant tags for each image. From those generated tags, we compute Word2Vec features similarly as above.

B. Feature learning, fusion, and classification

Each modal-specific feature vector is passed through a multilayer perceptron (MLP), which contains up to two fully connected layers, for higher-level feature learning. Another important benefit of these MLP is that they allow to control the output feature dimension (i.e., by choosing a relevant number of hidden units in the last layer of each MLP). Thus, even if the original feature dimensions are highly unbalanced: 4096 for CNNs, 1024 for IC features, and 300 for Word2Vec, they have all balanced dimensions after this first learning phase (features with higher dimension are reduced to match the lowest feature dimension). These balanced modal-specific features are then combined by vector concatenation to form a multimodal feature vector. Note that we also tried direct concatenation of all original features without dimension reduction, meaning that no modal-specific learning was performed, but performances were lower.

The fused multimodal feature is fed to an MLP with two fully connected layers for the higher-level multimodal feature learning step. A final classification layer is added (i.e., softmax [START_REF] Bishop | Pattern Recognition and Machine Learning (Information Science and Statistics)[END_REF] followed by quantization) to produce the binary prediction results. For each MLP in the global workflow, we retain a single set of parameters after some investigation on the performances (RMSProp, lr=0.0001, batchsize=512, ReLu, dropout=0.5). Optimization was conducted on validation loss.

IV. EXPERIMENT RESULT AND DISCUSSION

We performed experiments with the use of different input features and their combinations as summarized in Table I. Results are presented in terms of accuracy for the training, validation and test sets. It clearly appears that performance remains quite similar for the three sets, showing that no over-fitting occurred and that the investigated models generalize well to new data. From models based on individual features (first four rows), it is shown that IC features do bring valuable information for the task as they perform quite as good as classic VGG16 features. Furthermore, both are complementary (see 5th row) and reach a high accuracy of more than 83% on the test set, proving that IC features bring more semantic information to the task. Nevertheless, social information collected from LaFin tags alone outperforms all other specific features with a final accuracy of 89.63% on the test set. Note that we did also experiment with textual features extracted from LaFin images' titles, by similarly generating Word2Vec features. Nevertheless models with such textual features led to lower performances than with tags. Thus we did not consider them in the remaining experiments. Contrary to Flickr tags, generated tags were not able to capture high predictive information: used alone they reach a low accuracy of 65% on both the train and test sets and seem to be redundant with classic VGG16 features as the combination does not really bring any benefit (see 6th row). Going further with the combination of features, we then tested VGG166 and Word2Vec features together and, in the case of Flickr tags, got an improvement of accuracy (91%) that shows that both features contain significantly different information. A full combination of all three VGG16, IC features and Flickr tags further slightly improves the overall accuracy (92.6% on the test set). As a conclusion, this last model successfully predicts LaFin interestingness labels, thanks to a combination of both content and social information. These results are to be compared with the work of [START_REF] Dhar | High level describable attributes for predicting aesthetics and interestingness[END_REF] which obtained a best recall value of 70% for a precision value of roughtly 85% on a dataset of 40k Flickr images. We have shown that the use of contextual information in the form of image tags enables to increase the performances of socially-driven interestingness prediction. Nevertheless, it must be noted that the single use of this information alone despite all the other social metadata available did perform really well, although it seems that Flickr's assessment of interestingness was based on more than only the image tags. One interpretation for this might be that contextual information is redundant in the available metadata, e.g., the number of views is partially correlated with the tags, comments, etc. The high performance of our classifiers for LaFin dataset also proves that the two classes, non interesting and interesting, are separable and that our strategy of collecting the non interesting images was correct, even though we did not have access to the description of Flickr's interestingness prediction. This reinforces the quality of LaFin dataset that is publicly released to the community.

V. CONCLUSION

In this paper we disclosed a new large-scale socially-driven interestingness dataset (LaFin) collected from Flickr website and presented an analysis of the associated metadata. We built computational models for interestingness prediction where different types of features derived from different sources of information were taken into account. The prediction results revealed the correlation between contextual information and socially-driven interestingess. Future work may be devoted to re-annotate the LaFin dataset with a protocol for contentdriven interestingness inspired from [START_REF] Demarty | Mediaeval 2017 predicting media interestingness task[END_REF] and compare the dataset's social labels with content-driven labels. Then a subsequent in-depth study of the differences between these two notions of interestingness would be of great interest.

Fig. 1 .

 1 Fig. 1. General workflow of the proposed computational models.

http://www.multimediaeval.org/mediaeval2017/mediainterestingness/

https://www.flickr.com/explore/interesting/

http://www.acmmm.org/2017/challenge/social-media-prediction/

https://www.technicolor.com/dream/research-innovation/lafin-dataset. Please note that because of copyright issues, we do not release the images but their links to Flickr's web site.

https://github.com/gyglim/personalized-highlights-dataset

http://www.acmmm.org/2017/challenge/social-media-prediction/

https://www.flickr.com/

https://www.flickr.com/services/api/flickr.interestingness.getList.html

https://www.flickr.com/explore

http://mscoco.org/