How independent are our online content exposures? Using data from Understanding Society’s Innovation Panel Twitter Study (2007–2023), we reconstruct shared exposure networks of survey participants based on their engagement with tweets, accounts, and topics. This approach enables us to assess the extent to which two randomly selected individuals from a nationally representative sample are connected online—even when offline links are remote. Our preliminary analysis reveals that, on average, 32% of the Twitter accounts respondents engage with are shared with other survey participants,. We further explore how shared exposure varies by the type of platform behaviour (e.g., retweets and replies), while controlling for engagement metrics to mitigate biases from viral content. In addition to account-based networks, we construct content-based networks by leveraging transformer models and word embeddings to derive latent topics from retweeted content. Each individual’s topic profile is created by aggregating the topic distributions of the tweets they engage with, allowing us to cluster users based on the mix of content they consume. We then examine whether these content clusters are independent of—or driven by—socioeconomic gradients such as age, occupation, and income. Our findings challenge traditional assumptions of respondent independence in survey research and offer novel insights into how digital environments both reflect and transcend offline social structures. Implications for social network research and digital survey methodologies are discussed.

Social media platforms have become a frequent forum for uncivil discourse. We conceptualise incivility as language containing ill-mannered expressions, insults, swear words, or disrespectful attacks against a person or group. This language undermines democratic discourse and contributes to the fragmentation of public conversation. This paper explores the use of uncivil language on Twitter during the Brexit referendum campaign (6 January - 23 June 2016). This was a period of renegotiating the UK-EU relationship that greatly divided the UK public. We analyse the temporal patterns of uncivil language using 23M Tweets, tracking how the tone of public conversation shifted as the referendum date approached. To classify Tweets, we fine-tune the BERTweet model on 30,000 annotated Tweets, which were created using professional annotators from Appen. The analyses revealed significant regional differences in the prevalence of incivility, which fluctuated as key political events and statements shaped public sentiment. To further understand the dissemination of uncivil language, we visualise retweet networks, mapping how public officials became the target of incivility over time. Our findings demonstrate that while incivility was pervasive throughout the campaign, its intensity varied by region and was strongly influenced by interactions with political elites.

The alarming rise of hostile rhetoric targeting both (im)migrants and marginalised groups has permeated online discussions, particularly across social networking platforms. These digital arenas, notably popular sites like Facebook and X, have unfortunately become breeding grounds for the dissemination of prejudiced views and intolerance, often fueled by misinformation and divisive narratives. This research critically examines the prevalence of intolerance and xenophobic discourses within the Italian social media landscape. By systematically collecting freely posted comments, we employ a community detection procedure on a term-by-term matrix to uncover the primary issues that emerge from these online debates. Furthermore, we use a Bayesian Belief Network (BBN) to elucidate, from a probabilistic perspective, the intricate relationships between these issues and other relevant covariates, including the discourse's emotional valence and propagation dynamics. This comprehensive integration not only facilitates a causal inference approach but also unveils the key drivers and amplifiers of hateful and racist discourse, thereby underscoring the urgent need for informed intervention strategies against digital hate.

This work is part of the research project PRIN-2022 PNRR “Identification and Critical Analysis of Online Racism and Xenophobia against (Im)migrants and Roma people” (Project Code: P2022APKJL), funded by the European Union – Next Generation EU.

Introduction

Online incivility disrupts digital interactions, negatively affecting users' mental well-being and engagement. Current detection methods rely on natural language processing (NLP) and keyword-based filtering, often producing high false positive rates. This study integrates Graph Neural Networks (GNN) with Social Network Analysis (SNA) and NLP to enhance incivility detection while minimizing impacts on everyday discourse.

Literature Review

Prior research highlights the detrimental effects of incivility, including increased polarization, emotional distress, and disengagement. Traditional NLP-based classifiers primarily focus on textual content but fail to consider relational context and user interactions, leading to misclassification of sarcasm, nuanced discussions, and indirect hostility. Although Graph Neural Networks (GNNs) and Social Network Analysis (SNA) have been used separately in social computing, there is a lack of studies integrating these techniques to enhance context-aware incivility detection. Our research addresses this gap by combining text-based, network-based, and deep learning-based approaches to improve accuracy.

Research Methodology

We collected 3,210 comments from Reddit’s "worldnews" subreddit and labeled them. Our GNN model incorporated NLP-derived sentiment scores as edge features and SNA metrics (e.g., centrality, in-degree) as node features. The trained model was evaluated using F1-score, precision, and recall.

Findings

Our GNN model achieved an F1-score of 78.42%, outperforming traditional NLP models. Integrating network metrics significantly improved incivility detection accuracy, reducing false positives while maintaining high recall.

Discussion and Contributions

This study advances computational social science methodology by integrating deep learning and network structure approaches to moderate online incivility in social media. Our approach leverages existing methods by offering a hybrid method to manage online incivility. Practically, our approach can improve content regulation strategies, providing a hybrid approach for managing civility on social media.