Counter- TWIT : An I talian Corpus for Online Counterspeech in Ecological Contexts (2022)

Abstract: This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia. The main novelty is an annotated dataset comprising relevant tweets in their context of occurrence. The corpus is made up of tweets and responses captured by different profiles replying to discriminatory content or objectionably couched news. An annotation scheme was created to make explicit the knowledge on the dimensions of toxic speech and counterspeech.An analysis of the collected and annotated data and of the IAA that emerged during the annotation process is included. Moreover, we report about preliminary experiments on automatic counterspeech detection, based on supervised automatic learning models trained on the new dataset. The results highlight the fundamental role played by the context in this detection task, confirming our intuitions about the importance to collect tweets in their context of occurrence.

## introduction billions of users are active every day on the main social media platforms and they are regularly exposed to toxic discourse, i.e. speech that inflicts psychological or emotional harm and/or incites people to participate in bigoted practices ranging from sexism to homophobia, to racism. to protect users from online toxicity, social media providers have been increasingly implementing censorship-based measures. such measures are highly controversial and only targeted to the most extreme and explicit forms of toxic speech. implicit toxic contents are particularly dangerous because they can go under the radar, they are hard to question, and may end up being accepted without conversation participants fully realizing it. the question arises: how can we counter online toxic speech? recent studies in social philosophy of language investigated the strategy that consists in engaging in interventions aimed at avoiding that toxic contents get (wittingly or unwittingly) accepted by the conversation participants. such strategy is often dubbed counterspeech and has been mostly analyzed by taking into account face-to-face exchanges. philosophers of language @xcite have focused on how counterspeech could work in idealized conversational models. in particular, they have focused on speech that counters implicit toxic contents by (i) spelling out, unpacking, articulating the objectionable contents implicitly conveyed by a given utterance and then (ii) challenging, questioning, rejecting, disputing, confronting it. this counterspeech strategy seems very costly. the first move is cognitively costly: it's hard to unpack implicit content on the spot. the second move is about social cost: it may be tough to go and take a confrontational attitude. interestingly, certain features of how communication works on social networks make social media particularly interesting venues to easily observe real instances of counterspeech in ecological contexts. for counterspeech to succeed in face-to-face interactions, the counterspeaker needs to be ready to intervene saying the right thing, in the right place, at the right moment. on social networks, on the other hand, counterspeech can well be asynchronous: this may lighten its cognitive load. as for the social cost of counterspeech, note that social network users enjoy a bit of anonymity in their online intervention and online interactions follow a different etiquette than face-to-face exchanges in terms of interruption of the "conversation". this may possibly lighten the social cost associated with counterspeech. a further interesting aspect is that online counterspeech can reach many more people than offline interventions. in fact, users often challenge offline contents (newspapers articles, pieces of public speeches, reported conversations, passages of textbooks, and so on) on social networks, in order to give their conversational moves more attention. studying counterspeech online comes with the added benefit of enabling the researcher to build computational models of language interactions involving toxic speech and counterspeech. by leveraging the most recent natural language processing techniques, a corpus of counterspeech represents the first step towards automated systems to detect, support or even generate effective responses to toxic speech online. the exploratory theoretical investigation conducted in philosophy raises many empirical questions. in our work, we address a few ones. for instance: do people on social networks ever employ such an idealized model where in order to reject implicit toxic content one has to first make explicit what was wrong with it? or do users prefer less sophisticated strategy, like insulting and attacking bigoted contributions? does the use of irony make the counterspeaker sound more or less aggressive? do users support counterspeakers with reactions and comments or is it a solitary enterprise? many more questions are still left unanswered, but this work paves the way for illuminating further the nature and working of online counterspeech. the contributions of this article can be summarized as follows: ## related work there is a growing concern among the ict (information and communication technologies) companies leading the development of social networks about toxic speech: as it can undermine the image of such social environments as "safe" place, they must implement methods to cut off this phenomenon @xcite . some countries started to considered hate speech as a crime and sentencing it as such foot_0 . in other cases, institutions invited the ict companies to subscribe codes of conduct concerning hate speech moderation and censorship on their platforms. this is the case of the code of conduct issued by the eu commission in 2016 @xcite . moreover, social networks regulated hateful conduct, publishing guidelines to avoid harmful behaviors subscribed by users as part of their terms of service foot_1 . however, such measures don't seem to suffice to effectively combat the phenomenon @xcite . approaches to counterspeech have been investigated by the computational linguistics community, suggesting that counterspeech can reduce or limit the hateful content on the web, especially in social networks @xcite . however, especially from a computational point of view, the development of corpora and models for the automatic detection and generation of counterspeech is still underdeveloped, while most of the efforts have been devoted to the detection of various forms of toxic speech, hate speech included @xcite . most literature focuses on english language and considers toxic speech data collected from specific templates, which limits the coverage of explicit toxic speech and leaves out implicit toxic speech altogether. @xcite recently created a large multilingual corpus of short texts in english, french and italian, called conan, consisting @xmath0, with the effort of more than 100 operators from ngos and with a special focus on anti-muslim hatred online in different european countries. annotated corpora like conan enable a systematic study of counter-narratives (cns), a study which is still in its beginnings, but differs from the one we presented here. in particular, counterspeech in conan is not observed in an ecological setting, which is the perspective we hold in the current study. a similar work to @xcite is realized by @xcite , where off-the-shelf nmt models are used to synthesize silver data from other languages using the conan dataset as kick-start for generation to overcome the scarcity of gold standard data for training and the lack of huge datasets made of counter narratives in italian language. the accomplishment is done under different resource conditions, testing the effect of using (i) silver data, (ii) gold standard data, and (iii) their combination. tekiroglu et al. (2020) investigate methods to obtain high quality counter-narratives while reducing efforts from experts trained by some non-governmental organizations (ngos) to intervene in online hateful conversations. @xcite created benchmark data for training and evaluating the performance of an automatic detection system of counterspeech debates in order to introduce a novel nlu task. @xcite propose a study to understand how the counterspeech phenomenon is related to statistics of comments collected from youtube. @xcite present experimental results obtained considering different methods with and without context referring to abusive vs. not abusive tweets. unlike the related works presented in this section, the contribution of this work in automatic counterspeech detection is the development of a multi-layer corpus of italian twitter data in the context of their conversation thread. ## the counter-twit corpus we developed a novel corpus, called counter-twit, to study counterspeech online in an ecological setting, based on twitter conversation threads in the italian language. ## evaluation we carried our a battery of experiments in order to perform three independent binary classifications: toxic vs. non-toxic speech, counterspeech vs. not counterspeech, and support to counterspeech vs. not support to counterspeech. we employ a supervised classifier based on bert @xcite pre-trained on a large corpus of italian tweets named alberto @xcite . the metrics used to evaluate alberto's performance are precision, recall, and f1-score for the individual labels, and their macro-average. the three experiments are 5-fold crossvalidation experiments with 9 fine-tuning epochs and a learning rate of 10 -5 . the results are shown label prec.(0) rec.(0) f1 (0) prec.(1) rec.(1) f1 (1) prec. (avg) rec.(avg) f1 (avg) counterspeech .914 .884 .898 .441 .408 .402 .661 .663 .650 toxic .978 .985 .981 .295 .183 .186 .637 .584 .584 support .932 .929 .930 .550 .500 .501 .741 .714 .716 in the results are obtained with the model finetuned only with the tweet or reply text in isolation. we performed an additional experiment taking into account the root of the conversations where the replies belong. we do so by concatenating the text of the reply to the text of the original tweet it replies to, with the goal of observing how the performance of the model changes when considering the context of the reply. the results of this second experiment are shown in including context in the training improves the classification of counterspeech. this is due mainly to a higher recall on the positive class. this is true for all labels, and particularly for counter-speech, which is about 65% higher. however, the extra training data seem to confuse the classifiers for the other two labels. ## error analysis in order to get some deeper insight about the difficulties in classifying a counterspeech content, we selected false positives (fp), i.e., counterspeech tweets hat have not been classified as such by the model, and exploited the information included in the finer-grained annotation layer regarding counterspeech categories, namely explic-itation, hostility, irony/humor, alter-native. we considered all the fps for the first annotation layer, counting all the data (tweets or reply) that were labeled as belonging to the counterspeech category from humans but not from the model. thus, for those tweets we checked the values attached to the counterspeech typology labels in order to find a meaning among the classification errors and the counterspeech typologies' relation. the proportion of false positives over all the predictions obtained from the language model is the following: false positives represent about 7% of the total. of these, the vast majority are ironic (∼34%) and hostile (∼76%), also considering that the labels are not mutually exclusive. this qualitative analysis can lead to affirm that the model tends to confuse hostile and ironic content more than explicit and suggestion of alternative ones probably due to a higher cost from a cognitive and social point of view. there are two layers of complexity that give rise to disagreement in classifying correctly the tweets. detecting toxic speech depends on how each subject is sensitive to detecting each axis of discrimination (which often varies along demographic and psychological factors). a further source of disagreement stems from the relative unconstrained character of the notions deployed (toxic speech and counterspeech) @xcite . finally, we analyzed the false positive rate by counterspeech category. irony and hostility are by far the most difficult categories to predict, with a fp ratio of about 60% and 70% respectively, while next to no fps are predicted for explicit and alternative. ## discussion and conclusions in this work we studied hate speech in online environments. to address the dangers of toxic speech, social networks defined policies that regulate speech inciting hatred, while some countries started to introduce norms to treat this phenomenon as a crime and sentenced as such. this way to address the problem showed some limitations as the main approaches consist in blocking or suspending the problematic content or the user account itself. therefore several involved parties, such as institutions and organizations, started to consider counterspeech as an alternative to blocking @xcite . thus, adding "more speech" has been considered as a valid alternative to counter hate speech. we collected and annotated data from twitter in label prec.(0) rec.(0) f1 (0) prec.(1) rec.(1) f1 (1) prec. (avg) rec.(avg) f1 (avg) counterspeech .960 .883 .920 .466 .730 .564 .713 .807 .742 toxic .979 .840 .903 .037 .283 .065 .508 .561 .484 support .922 .816 .865 .317 .544 .396 .620 .680 .630 we developed the counter-twit corpus made of tweets and replies collected from accounts that has been selected after a deep research based on shared contents. all the data collected have been annotated, by exploiting a web-based annotation platform developed roughly from the scratch and published online foot_5 , where a group of expert annotators were applying a novel multi-layer annotation scheme devoted to mark whether the tweets or replies were counterspeech, toxic speech or in support of counterspeech (layer 1). in case counterspeech was marked as present, users were asked to label the text as belonging to some typology of counterspeech for the sake of a deeper understanding of the phenomenon (layer 2). thus, the annotated corpus has been used for training the alberto neural language model for performing a battery of binary classification task related to the detection of toxic, counterspeech, and support to counterspeech. we used this language model since it has been trained and developed using an italian vocabulary instead of using other multilingual model that presented limitations to the type of language learned and the size of vocabulary @xcite . we executed two type of experiments: one using only the replies of conversation tree and the second with also the "main" tweet. this approach has been designed in order to go deep into the intuition that this classification task needs the context. results show that performance, recall in particular, improves when conversation context data are provided, and this supports the original hypothesis that counterspeech must be studied in a context, which is intuitive given the definition of counterspeech as second-turn intervention aimed to contrast a previous contribution @xcite , taken as reference definition in this work. finally, we performed a statistical and qualitative evaluation of the results obtained from the neural language model evaluating the number of data classified as not belonging to counterspeech class rather than being considered as such (false positives data). we discovered that the model tends to confuse most with irony and hostility labels rather than explicitation and suggestion to alternative ones. given the promising preliminary results, we plan to expand the corpus in our future research. furthermore, other qualitative analysis could be run by considering the correlation of types of counterspeech and the predictions made with a language model in order to understand in greater detail how the model behaves towards a specific counterspeech category. indeed, annotating content as counterspeech is not an easy task, due to different shapes of the textual meaning based on the context and the language used. there is not a unique pattern to individuate and mark the tweet as belonging to a specific categories. a large annotated corpus will provide a more solid base for training the model in detecting counterspeech and, in possible future developments, for generating automatically counterspeech content in order to fight hate speech, which is another very interesting direction @xcite . counter-twit foot_6 is made available online to further study the phenomenon described and other issue related to counterspeech classification in italian twitter.