Predicting Social Exclusion: A Study of Linguistic Ostracism in Social Networks
Authors: Greta Gandolfi, Carlo Strapparava (Università di Trento, Fondazione Bruno Kessler, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Ostracism is a community-level phenomenon, shared by most social animals, including humans. Its detection plays a crucial role for the individual, with possible evolutionary consequences for the species. Considering (1) its bound with communication and (2) its social nature, we hypothesise the combination of (a) linguistic and (b) community-level features to have a positive impact on the automatic recognition of ostracism in human online communities. We model an English linguistic community through Reddit data and we analyse the performance of simple classification algorithms. We show how models based on the combination of (a) and (b) generally outperform the same architectures when fed by (a) or (b) in isolation.

Introduction
Ostracism is a social phenomenon meant to ignore or exclude an individual from a group, performed by an individual or a group. Due to its relevance in our everyday life - as a threat to basic needs AUTHOR - and its impact on community-level essential patterns - such as mother-infant attachment, xenophobia, and leadership AUTHOR - each person must develop a system to predict and avoid it. Humans and other social animals (such as rhesus monkeys, for example) use ostracism as a form of social control on problematic group members, as a way to strengthen their group and to remove members that do not conform to social norms. Moreover, it reinforces the hierarchical role of the perpetrators while causing the social or even the actual death of their direct victims. For these reasons, the scope of ostracism allows researchers to assume that its identification has adaptive advantages AUTHOR. Given its intrinsic relation with communication and its community-level impact, we assume that its detection can be automatised relying on linguistic and extra-linguistic, community-level, social features. We expect both the types of information to be predictive but to work best when combined. Reddit communities can be used as proxies of linguistic communities since they provide huge amounts of linguistic data paired with social information. The performance of minimal binary classifiers, such as Naive Bayes and SVM, can be investigated to analyse the relevance of such cues to distinguish between prospective ostracised or not-ostracised members of a group, modelling our adaptive ability to detect ostracism in advance.

Conclusion
We explored the phenomenon of social exclusion through Reddit data within a period of 9 years. We collected reactions addressed to moderators, here considered as leading figures of the groups. We selected 10 moderators that left the community influenced by the linguistic and non-linguistic behaviour of the group they lead. We performed a binary classification task on a total of 14200 linguistic reactions addressed to each of the target moderators, analysing the influence of linguistic and extra-linguistic or social patterns on two simple models' performance. We showed how the performance of both models increases if linguistic and extra-linguistic features are combined. The best combination of features, concerning the SVM model, is given by the combination of all the linguistic features and all the social features considered. We can consider this work as an attempt to follow the statements of the sociolinguistics that considers language as intrinsically bound up with society AUTHOR. Our experiment and the relative techniques are simple and easy to replicate. We think that they can be also applied in non-English domains, just using a translating system for the stop-words. All the other features can be directly generalised to other languages.