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In the digital era, individuals are increasingly profiled and grouped basedon the traces they leave behind in online social networks such as Twitter andFacebook. In this paper, we develop and evaluate a novel text analysis approachfor studying user identity and social roles by redefining identity as asequence of timestamped items (e.g. tweet texts). We operationalise this ideaby developing a novel text distance metric, the time-sensitive semantic editdistance (t-SED), which accounts for the temporal context across multipletraces. To evaluate this method we undertake a case study of Russianonline-troll activity within US political discourse. The novel metric allows usto classify the social roles of trolls based on their traces, in this casetweets, into one of the predefined categories left-leaning, right-leaning, andnews feed. We show the effectiveness of the t-SED metric to measure thesimilarities between tweets while accounting for the temporal context, and weuse novel data visualisation techniques and qualitative analysis to uncover newempirical insights into Russian troll activity that have not been identified inprevious work. Additionally, we highlight a connection with the field ofActor-Network Theory and the related hypotheses of Gabriel Tarde, and wediscuss how social sequence analysis using t-SED may provide new avenues fortackling a longstanding problem in social theory: how to analyse societywithout separating reality into micro versus macro levels.

Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter