Pinar Arslan
email: pinar.arslan@inria.fr

Michele Corazza
email: michele.corazza@inria.fr

Elena Cabrio

Serena Villata
email: serena.villata@unice.fr

Overwhelmed by Negative Emotions? Maybe You Are Being Cyber-bullied!

Keywords: Computing methodologies → Natural language processing; Cyberbullying detection, emotion classification, sentiment analysis, social media

With the increasing number of interactions, social media users have been vulnerable to intentional aggressive acts and cyberbullying instances. In this paper, first, we carry out a message-level cyberbullying annotation on an Instagram dataset. Second, we use the correlations on the Instagram dataset annotated with emotion, sentiment and bullying labels. Third, we build a message-level emotion classifier automatically predicting emotion labels for each comment in the Vine bullying dataset. Fourth, we build a session-based bullying classifier with the use of n-grams, emotion, sentiment and concept-level features. For both emotion and bullying classifiers, we use Linear Support Vector Classification. Our results show that "anger" and "negative" labels have a positive correlation with the presence of bullying. Concept-level features, emotion and sentiment features in different levels contribute to the bullying classifier, especially to the bullying class. Our best performing bullying classifier with n-grams and concept-level features (e.g., polarity, averaged polarity intensity, moodtags and semantics features) reaches to an F1-score of 0.65 for bullying class and a macro average F1-score of 0.7520.

defined as "an aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself" [START_REF] Slonje | Cyberbullying: Another main type of bullying?[END_REF]. Many cyberbullying victims struggle with emotional burden, such as emotional stress, in that these victims may often face being threatened or offended in social media platforms. The automatic detection of cyberbullying, therefore, has benefits to the society, including avoiding vulnerable individuals' (e.g., teenagers) encounter with cyberbullying, and hence, minimizing any potential mental health conditions directly or indirectly caused by cyberbullying.

The main contribution of this paper is threefold. First, we provide a 1000-comment Instagram dataset annotated for cyberbullying, emotion and sentiment per Instagram post. With this dataset, we identify which emotion and sentiment features correlate with bullying instances. Second, we build a message-level emotion classifier which is then used to automatically predict emotion labels for each comment of the bullying dataset. Third, we build a sessionlevel bullying classifier with the use of the following features: ngrams, emotion and sentiment features, message-level emotion and concept-level features. The impact of these features on the bullying classifier is unveiled. For both classifiers, Linear Support Vector Classification is implemented. Although emotion and sentiment features were employed in the cyberbullying detection tasks in the literature [START_REF] Nahar | Semi-supervised learning for cyberbullying detection in social networks[END_REF][START_REF] Xu | Fast learning for sentiment analysis on bullying[END_REF], our study involves a larger set of emotion and sentiment features in different levels from various resources (e.g., Emolex, SenticNet).

TASKS 2.1 Correlation Analysis

In order to investigate the relationship of emotion and sentimentrelated information with bullying instances, Pearson's correlation coefficient and 2-tailed p-value were measured on an Instagram dataset. Specifically, a portion of Instagram dataset [START_REF] Hosseinmardi | Analyzing labeled cyberbullying incidents on the Instagram social network[END_REF] was annotated per post. This comment-level annotation revealed explicit correlation of bullying with emotion and sentiment features. We randomly selected 10 media sessions1 (i.e., 5 bullying and 5 no bullying sessions) from the Instagram dataset. Two annotators from linguistics annotated 1000 Instagram comments, which were obtained from 10 sessions, with emotion, sentiment and bullying labels. The annotation was addressed using the following emotion, sentiment and bullying labels: anger, fear, joy, sadness, other, no emotion, positive, negative, neutral, bullying and no bullying. We computed the inter-annotator agreement on a subset of the annotated dataset using Cohen's Kappa. We obtained κ = 0.668 for emotion, κ=0.694 for sentiment, and κ=0.708 for the bullying annotations, meaning substantial agreement for all the tasks. Here is an example of annotated comment2 with the labels anger, negative and bullying: "Shove off baby ugly @username". Table 1 shows the frequencies and percentages of the annotated labels for the sessions with overall bullying and no bullying labels.

Baseline Systems and Pre-processing

We use two baselines: majority-class and n-gram based baselines applied to 10-fold cross-validated emotion and cyberbullying datasets.

Our emotion dataset with 2808 messages and 5 emotion labels (i.e., 577 "anger", 567 "fear", 690 "joy", 397 "sadness", 577 "other") was the combination of the WASSA-2017 [START_REF] Saif | WASSA-2017 Shared Task on Emotion Intensity[END_REF] dataset (training and development sets) 3 and the annotated Instagram dataset where the "no emotion" tags were renamed as "other" tags. Our cyberbullying dataset with 970 sessions (i.e., 304 "bullying", 666 "no bullying") was the Vine dataset [START_REF] Rahat Ibn Rafiq | Careful what you share in six seconds: Detecting cyberbullying instances in Vine[END_REF]. We used the word unigram-based emotion classification baseline on which only word tokenization was applied.

Our cyberbullying classification baseline system was based on word (1,2) and character (3, 4, 5) n-grams. We implemented LinearSVC for the n-gram based baseline systems with TF-IDF weighting schemes. We addressed the following pre-processing steps for the cyberbullying baseline system, emotion and cyberbullying classification systems: cleaning the format of texts, word tokenization, tagging of URLs and usernames, removal of hashtag and stopwords, addition of a whitespace before and after punctuations, use of placeholders for adversative conjunctions, negative items and numbers, and stemming.

Emotion and Cyberbullying Classification

Our message-level emotion 4 and session-based bullying classifiers were based on LinearSVC. After a hyperparameter search, we selected penalty parameter C as 1.0 and class_weight as balanced.

A 10-fold cross-validation was applied to the emotion and cyberbullying datasets. Upon building an emotion classifier with optimal performance, the whole emotion dataset was used as a training set and the emotion classifier was tested on the whole bullying dataset.

In this way, we obtained automatically predicted emotion labels for each Vine comment. The following features were experimentally tested for the two classifiers: word n-grams (i.e., unigrams, bigrams), character n-grams (i.e., trigrams, fourgrams, fivegrams), emotion and sentiment features (i.e., word-level emotion and sentiment tags extracted from EmoLex [START_REF] Saif | Crowdsourcing a word-emotion association lexicon[END_REF]), SenticNet features (i.e., polarity, averaged polarity intensity per message or session, moodtags, semantics features obtained via SenticNet [START_REF] Cambria | SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings[END_REF] knowledge base and input concepts), and message-level emotion features (i.e., automatically predicted emotion features used for the bullying classifier). We tested the contribution of each individual feature and concatenated the features on the classifiers. Except for the averaged polarity intensity, TF-IDF weighting schemes were applied to all the features. Our best performing emotion classifier is comprised of the first four features. Our best performing cyberbullying classifier was based on word and character n-grams used with concept level SenticNet features.

RESULTS AND DISCUSSION

The correlation results reveal a strong positive association for the pairs "anger-'bullying" (r=0.6805, p <0.05) and "negative-bullying" (r=0.5631, p <0.05), a small negative association for the pairs "joybullying" (r=-0.1609, p <0.05), "no emotion-bullying" (r=-0.2429, p <0.05), "positive-bullying" (r=-0.1696, p <0.05), "neutral-bullying" (r=-0.2668, p <0.05) and "other-bullying" (r=-0.0854, p <0.05). The emotion labels "sadness" and "fear" have no significant correlation with the bullying instances. This lack of association might have stemmed from the fact these labels were only very few in number. We can conclude that bullying bearing messages can be detected more easily based on the emotion and sentiment labels of the messages. We experimented various features in isolation and group on the emotion and cyberbullying classifiers. McNemar's test was applied to compare significant differences between the two systems based on the contingency table. Table 2 presents the results of emotion classification systems with macro average F1-scores 5 . The biggest contribution of individual features was obtained with word unigrams (i.e., 0.79), character fourgrams (i.e., 0.78) followed by character fivegrams (i.e., 0.77). The system with word unigrams was significantly different from the one with character fourgrams and fivegrams. The best performing emotion classifier (i.e., 0.82) with all features showed a significant difference compared to the baseline systems and the system with word unigrams. This suggests each single feature contributes in different aspects rendering a more sensitive emotion classification possible.

Table 3 displays the macro average F1-scores of the cyberbullying classification systems. The individual feature with the highest F1-score was character fourgrams (i.e., 0.7376). The system with word n-grams and character trigrams (i.e., 0.7497) was the only one system with a significant difference from the n-gram based baseline. The highest F1-score (i.e., 0.7520) was obtained with the system containing all n-gram features and SenticNet features, which shows the importance of polarity, averaged polarity intensity, moodtags and 5 F1-scores in bold show the best performing models. semantics on the bullying classifier. SenticNet (i.e., 0.65) and predicted emotion features (i.e., 0.64) had larger impact than EmoLex features (i.e., 0.63) on the bullying classifier. The three features significantly increased the performance of bullying class while they decreased the performance of the no bullying class. This proves that the bullying class necessitates emotion, sentiment and semantics features to perform well while the no bullying class gets better with the use of more abstract features such as character n-grams. The system with the highest macro average F1-score of 0.7520 had 25% error rate which might stem from the words with multiple EmoLex labels (e.g., bitch: sadness, anger, fear, disgust and negative), incorrectly predicted emotion labels, idiomatic expressions (e.g., shove up your ass) and internet slangs (e.g., Gtfo). Our findings show that SenticNet features including emotion, sentiment and semantics-related features, are useful complementary features, and hence, advisable while analyzing emotion and bullying in text classification problems.

CONCLUSION AND FUTURE WORK

In this study, we carried out the first (to the best of our knowledge) message-level cyberbullying annotation on an Instagram dataset. Each Instagram post was annotated with bullying, emotion and sentiment labels. Second, we calculated correlations on the annotations, which unveiled positive correlations for the pairs "anger-bullying" and "negative-bullying" and negative correlations for several pairs (e.g., "no emotion-bullying"). Third, we adopted an approach to detect cyberbullying events, which was firstly to build a message-level emotion classifier and then to let the emotion classifier predict emotion labels for each Vine comment. We empirically showed that not only the correlation but also SenticNet, emotion and sentiment features in different levels impact on the cyberbullying detection There are several future research lines to be considered such as to include more emotional aspects from social media texts, extend our annotated dataset, add new features (e.g., word embeddings, slangs), use social network features, and tackle the issue of multilinguality.

Table 1 :

 1 Frequencies and percentages of annotated labels

	Labels	Bullying No Bullying Frequency Percentages
	anger	118	5	123	12.3%
	fear	2	0	2	0.2%
	sadness	9	4	13	1.3%
	joy	130	155	285	28.5%
	other	73	28	101	10.1%
	no emotion 284	192	476	47.6%
	positive	143	164	307	30.7%
	negative	158	12	170	17%
	neutral	315	208	523	52.3%
	bullying	61	0	61	6.1%
	noneBll	555	384	939	93.9%

Table 2 :

 2 F1-scores of emotion classification systems

	Majority Baseline	Unigram Baseline	word-1gram word-2gram char-3gram char-4gram char-5gram EmoLex SenticNet	Anger	Fear	Joy	Other	Sadness	Macro Average
	✓			0.00 0.00 0.39 0.00 0.00 0.08
		✓		0.20 0.50 0.46 0.65 0.32 0.45
			✓	0.79 0.85 0.81 0.72 0.78 0.79
			✓	0.45 0.52 0.65 0.57 0.44 0.53
			✓	0.73 0.78 0.79 0.75 0.72 0.75
			✓	0.76 0.83 0.81 0.76 0.76 0.78
			✓	0.76 0.83 0.80 0.75 0.74 0.77
			✓	0.51 0.64 0.69 0.68 0.57 0.62
			✓	0.73 0.84 0.76 0.68 0.78 0.76
			✓ ✓	0.77 0.84 0.81 0.74 0.78 0.79
			✓ ✓ ✓	0.76 0.83 0.82 0.77 0.76 0.79
			✓ ✓ ✓ ✓	0.77 0.85 0.83 0.77 0.79 0.80
			✓ ✓ ✓ ✓ ✓	0.78 0.85 0.83 0.76 0.78 0.80
			✓ ✓ ✓ ✓ ✓ ✓	0.77 0.85 0.83 0.76 0.78 0.80
			✓ ✓ ✓ ✓ ✓ ✓ ✓ 0.79 0.88 0.85 0.76 0.81 0.82

Table 3 :

 3 F1-scores of cyberbullying classification systems Vine dataset, improving the results of the cyberbullying classification task, especially for the bullying class.

	Majority Baseline	word-1gram word-2gram char-3gram char-4gram char-5gram EmoLex pred.emotion SenticNet	Bullying No Bullying Avg
	✓			0.00	0.81	0.4071
		✓		0.62	0.83	0.7277
		✓		0.36	0.83	0.5946
		✓		0.62	0.84	0.7333
		✓		0.62	0.85	0.7376
		✓		0.61	0.85	0.7290
		✓		0.56	0.66	0.6113
		✓		0.61	0.77	0.6875
		✓ 0.62	0.81	0.7154
		✓ ✓		0.59	0.84	0.7142
		✓ ✓ ✓		0.64	0.86	0.7497
		✓ ✓ ✓ ✓		0.63	0.86	0.7419
		✓ ✓ ✓ ✓ ✓		0.62	0.86	0.7374
		✓ ✓ ✓ ✓ ✓✓		0.63	0.85	0.7407
		✓ ✓ ✓ ✓ ✓ ✓		0.64	0.85	0.7466
		✓ ✓ ✓ ✓ ✓	✓ 0.65	0.86	0.7520
		✓ ✓ ✓ ✓ ✓✓✓		0.64	0.85	0.7442
		✓ ✓ ✓ ✓ ✓✓✓✓ 0.64	0.84	0.7412
	task addressed on the

A media session is the thread of comments following a picture.

Warning:The examples on the paper include very explicit language. These contents do not reflect the views of the authors. It is, however, necessary to use such data despite its offensive nature as it is the only way to find methods to automatically master this kind of contents on the Web.

We used the tweets with an emotion intensity score of 0.50 or higher.

A message is either a tweet or an Instagram comment for the emotion dataset, and it is a Vine comment for the bullying dataset.

ACKNOWLEDGMENTS

This work was funded by the CREEP project (http://creep-project. eu/), a Digital Wellbeing Activity supported by EIT Digital in 2018.


Why Not Socialism?, whose title refers to Albert Einstein's "Why Socialism?" (Monthly Review, May 1949), was published a few months before its author died, 5 th of August 2009, at the age of 68. This short book is an updated and substantially extended version of an article published a few years before (in Broadbent 2000, pp. 58-72). It puts an end to the long and rich career of the philosopher, which is mainly represented by the publication of the founding book on analytical Marxism (Karl Marx's Theory of History: A Defence, 1978) and by his major contributions to debates on self-ownership and left-wing libertarianism (Self-ownership, freedom and equality, 1995) as well as on social justice. Along with his two previous books, If You're an Egalitarian, How Come You're So Rich? (1999) and Rescuing Justice and Equality (2008) this book reflects his latest concern. The 2008 tome was part of a controversy over John Rawls' theory of justice, which he opposes for focusing on personal choices rather than on basic structures. Both books extend Jerry Cohen's long-lasting commitment to normative political philosophy. Why Not Socialism? is worth of interest for everyone, including scholars and students in philosophy, social sciences, economics, interested in issues related to socialism and to social justice. It is a philosophical essay in defense of socialism as a mode of organization of social relations on the basis of the principles of equality and community. It is written in a very lucid and sometimes humorous prose, as Cohen always did. His defense is founded on the metaphor of the camping trip as an implementation of socialism. He first describes, with his habitual precision and rigor, the way in which the principles are laid down. He then wonders whether socialism is desirable and feasible and focuses especially on market socialism. This metaphor can be seen as a reminder of the communist summer camp, called Kinderland, where Cohen went as a child and a teenager. This camping is presented as small-scale socialism, with collective ownership and mutual planning, where individuals act in cooperation with each other towards the satisfaction of a common interest. They put the principles of equality and community into practice and they exclude market exchange and private ownership in order to preserve social cohesion. The kind of equality favoured by Cohen in this short book is the socialist equality of opportunity, as opposed to the bourgeois equality of opportunity (equality of rights) and the leftliberal equality of opportunity (equality of social circumstances). Socialist equality of opportunity allows a situation where non-chosen disadvantages are excluded, whether they are social or natural. Hence different levels of income between individuals are condemned only if they reflect individual preferences and do not correspond to significant differences in terms of satisfaction. But if socialist equality of opportunity is on its own, it allows three kinds of inequalities to prevail, which proceed from different preferences, from regrettable choices and from option luck. Only the first kind is acceptable from the point of view of socialism. That is why the principle of equality, even of the most radical kind, as presented by Cohen, must be complemented by the principle of community, which refers to mutual responsibility and cooperation among individuals. Having explained the principles upon which socialism has to be founded, Cohen wonders whether socialism, notably the spreading of these principles on a national and international scale, is desirable and, more importantly, feasible. This desirability would depend on the ability of everybody to treat everybody else with a level of reciprocity that can be found in friendship. The issue of feasibility is more complicated, because of the difficulty of spreading these principles on a large scale, for two reasons that are related to the limits of human nature and social technology. Cohen claims that even if we can assume that a society founded on human generosity exists, the main difficulty would still be that we do not have any organizational technology, namely the mode through which socialism could be organized. "Our problem is that, while we know how to make an economic system work on the basis of development, and, indeed, the hypertrophy, of selfishness, we do not how to make it work by developing and exploiting human generosity" (p. 58). That is the reason why it is necessary to design models of socialism. With this goal in mind, Cohen bases his views on two restrictive assumptions. On the one hand, he criticizes "nineteenthcentury socialists" (p. 66) when he claims that historical experience does not allow a generalized central planning to be feasible, supposedly with reference to the Soviet and Chinese experiences. On the other hand, he assumes that the market motives of greed and fear, no matter how repugnant they are, are the "easiest way to generate productivity" (p. 76). His argument relies explicitly on two books [START_REF] Carens | Equality, Moral Incentives and the Market. An Essay on Utopian Politico-Economic Theory[END_REF][START_REF] Roemer | A Future for Socialism[END_REF] in discussing the benefits of market socialism. He wonders whether it might be possible to implement market socialism in such a way that the efficiency of market -based on its information function -is preserved and that market incentives -greed and fear -are excluded, together with the resulting unequal distribution. Market mechanisms would then be used to solve the problem of social technology on the behalf of equality and community. Yet he admits that less importance would here be given to these two principles than in traditional socialism, but he claims that they would still exist thanks to the development of a strong Welfare State and to the extended distribution of basic income. Still, for Cohen market socialism fulfills the socialist standards of capitalism better, even if it does less, than "the full socialist ideal" (p. 75). He favors market socialism because of its stronger feasibility, even if he admits being "agnostic" (p. 76) about the possibility of achieving socialism, namely the full existence of the free operation of personal choices on a large scale within the constraints of equality and community. He concludes, by referring to Einstein, that socialism is humanity's attempt "to overcome and advance beyond the predatory phase of human development" and he thinks that, even if humanity has up to now not succeeded in achieving it, the fight should not now be given up. Cohen's book treats, with elegance and virtuosity, philosophical issues that are both major and urgent. They are especially urgent within the context of the hardening contradictions of capitalism and the developing global economic crisis, especially in so far as its deadly outcome for the world population is concerned. Still, we may wonder whether the ambitions of Cohen's book suffer from some limitations that are due to the illusion that it is not possible to go beyond the market -he does not refer to capitalism -and that humanity has to deal with it. Such an illusion has been severely attacked and condemned by Marxists in the 19 th , 20 th and 21 st centuries. It seems then that Cohen's socialism, having given up the possibility of successfully achieving Marx's catch-phrase, "from each according to his ability, to each according to his needs!" as it appears in The German Ideology and Critique of the Gotha Program, can be presented as some adaptive preference due to the cost of "full" socialism that is judged too high. It must be added that if the consistency between his first book, Karl Marx's Theory of History, and now Why not socialism? is not immediately clear, we can see that both are ahistorical, and that the collapse of Cohen's illusions about a deterministic law of history has to do with his claim in Self-ownership and equality (p. 7) that "history has shredded [Marx's] predictions". He was thus led to turn to the search of moral values for advocating socialism, which he confirms in If you're an egalitarian, how come you're so rich? (1999). He claims there (p. 43) "that socialists must abandon the [Marxian] obstetric conception, and that they must, in some measure, be utopian designers". Jerry Cohen's long journey, which recently came to an end, had been a continuous search for intellectual tools in the service of social transformation. Cohen became a "utopian designer". Such a position can be seen as a break with the Hegelian method, probably under the influence of John Roemer, for whom exploitation does not have to do with the extraction of surplus labour, but with social injustice. Normative issues became his priority in the early 1990s. Referring to his debate with John Rawls' Theory of Justice, the issue was then to strike a balance between personal rights and social duties. In doing so, he acknowledged John Roemer's conclusion: "It is not at all clear how analytical Marxists will differ from non-Marxist philosophers like Ronald Dworkin and Amartya Sen" (1986, p. 191).