Profile Update: The Effects of Identity Disclosure on Network Connections and Language

Our social identities determine how we interact and engage with the world surrounding us. In online settings, individuals can make these identities explicit by including them in their public biography, possibly signaling a change to what is important to them and how they should be viewed. Here, we perform the first large-scale study on Twitter that examines behavioral changes following identity signal addition on Twitter profiles. Combining social networks with NLP and quasi-experimental analyses, we discover that after disclosing an identity on their profiles, users (1) generate more tweets containing language that aligns with their identity and (2) connect more to same-identity users. We also examine whether adding an identity signal increases the number of offensive replies and find that (3) the combined effect of disclosing identity via both tweets and profiles is associated with a reduced number of offensive replies from others

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Stance Polarity in Political Debates: a Diachronic Perspective of Network Homophily and Conversations on Twitter

[EN] In the last decade, social media gained a very significant role in public debates, and despite the many intrinsic difficulties of analyzing data streaming from on-line platforms that are poisoned by bots, trolls, and low-quality information, it is undeniable that such data can still be used to test the public opinion and overall mood and to investigate how individuals communicate with each other. With the aim of analyzing the debate in Twitter on the 2016 referendum on the reform of the Italian Constitution, we created an Italian annotated corpus for stance detection for automatically estimating the stance of a relevant number of users. We take into account a diachronic perspective to shed lights on users' opinion dynamics. Furthermore, different types of social network communities, based on friendships, retweets, quotes, and replies were investigated, in order to analyze the communication among users with similar and divergent viewpoints. We observe particular aspects of users' behavior. First, our analysis suggests that users tend to be less explicit in expressing their stances after the outcome of the vote; simultaneously, users who exhibit a high number of cross-stance relations tend to become less polarized or to adopt a more neutral style in the following phase of the debate. Second, despite social media networks are generally aggregated in homogeneous communities, we highlight that the structure of the network can strongly change when different types of social relations are considered. In particular, networks defined by means of reply-to messages exhibit inverse homophily by stance, and users use more often replies for expressing diverging opinions, instead of other forms of communication. Interestingly, we also observe that the political polarization increases forthcoming the election and decreases after the election day.The work of Viviana Patti and Giancarlo Ruffo was partially funded by the Fondazione CRT under research project the Hate Speech and Social Media (2016.0688), and the "Progetto di Ateneo/CSP 2016" under research project "Immigrants, Hate and Prejudice in Social Media" (S1618_L2_BOSC_01). The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project "MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech" (PGC2018-096212-B-C31).Lai, M.; Tambuscio, M.; Patti, V.; Ruffo, G.; Rosso, P. (2019). Stance Polarity in Political Debates: a Diachronic Perspective of Network Homophily and Conversations on Twitter. Data & Knowledge Engineering. 124:1-20. https://doi.org/10.1016/j.datak.2019.101738S12012

Stance classification of Twitter debates: The encryption debate as a use case

Social media have enabled a revolution in user-generated content. They allow users to connect, build community, produce and share content, and publish opinions. To better understand online users’ attitudes and opinions, we use stance classification. Stance classification is a relatively new and challenging approach to deepen opinion mining by classifying a user's stance in a debate. Our stance classification use case is tweets that were related to the spring 2016 debate over the FBI’s request that Apple decrypt a user’s iPhone. In this “encryption debate,” public opinion was polarized between advocates for individual privacy and advocates for national security. We propose a machine learning approach to classify stance in the debate, and a topic classification that uses lexical, syntactic, Twitter-specific, and argumentative features as a predictor for classifications. Models trained on these feature sets showed significant increases in accuracy relative to the unigram baseline.Ope