In the digital era, individuals are increasingly profiled and grouped based on the traces they leave behind in online social networks such as Twitter and Facebook. In this paper we develop and evaluate a novel text analysis approach for studying user identity and social roles by redefining identity as a sequence of timestamped items (e.g. tweet texts). We operationalise this idea by developing a novel text distance metric, the time-sensitive semantic edit distance (t-SED), which accounts for the temporal context across multiple traces. To evaluate this method we undertake a case study of Russian online-troll activity within US political discourse. The novel metric allows us to classify the social roles of trolls based on their traces, in this case tweets, into one of the predefined categories left-leaning, right-leaning, and news feed. We show the effectiveness of the t-SED metric to measure the similarities between tweets while accounting for the temporal context, and we use novel data visualisation techniques and qualitative analysis to uncover new empirical insights into Russian troll activity that have not been identified in previous work. Additionally, we highlight a connection with the field of Actor-Network Theory and the related hypotheses of Gabriel Tarde, and we discuss how social sequence analysis using t-SED may provide new avenues for tackling a long-
Sports games and other media events can induce very strong feelings of co-presence that can change communication patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dynamics. We employ a computational focus group method to identify 790,744 international Twitter users, and we track their behavior before and during the 2014 FIFA World Cup. We pick a set of Twitter users who specified the teams that they are supporting, such that we can identify communities of fans of the teams, as well as the entire community of World Cup fans. The structure, dynamics, and content of communication of these communities are analyzed to compare behavior outside and during the event and to examine behavioral responses across languages. Specifically, the temporal patterns of the tweeting volume, topics, retweeting, and mentioning behaviors are analyzed. We find similarities in the responses to media events, characteristic changes in activity patterns, and substantial differences in linguistic features. Our findings have implications for designing more resilient socio-technical systems during crises and developing better models of complex social behavior.
Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in local neighborhoods. This assumption however limits the generalizability power of GNNs. To address this limitation, we propose a flexible GNN model, which is capable of handling any graphs without being restricted by their underlying homophily. At its core, this model adopts a node attention mechanism based on multiple learnable spectral filters; therefore, the aggregation scheme is learned adaptively for each graph in the spectral domain. We evaluated the proposed model on node classification tasks over eight benchmark datasets. The proposed model is shown to generalize well to both homophilic and heterophilic graphs. Further, it outperforms all state-of-the-art baselines on heterophilic graphs and performs comparably with them on homophilic graphs.
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