| 1. |
- Hanteer, Obaida, et al.
(författare)
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From Interaction to Participation : The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter
- 2018
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Ingår i: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). - : IEEE Computer Society. - 9781538660515 ; , s. 531-534
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Konferensbidrag (refereegranskat)abstract
- In the context of community detection in online social media, a lot of effort has been put into the definition of sophisticated network clustering algorithms and much less on the equally crucial process of obtaining high-quality input data. User-interaction data explicitly provided by social media platforms has largely been used as the main source of data because of its easy accessibility. However, this data does not capture a fundamental and much more frequent type of participatory behavior where users do not explicitly mention others but direct their messages to an invisible audience following a common hashtag. In the context of multiplex community detection, we show how to construct an additional data layer about user participation not relying on explicit interactions between users, and how this layer can be used to find different types of communities in the context of Twitter political communication.
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| 2. |
- Vega, Davide, et al.
(författare)
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Foundations of Temporal Text Networks
- 2018
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Ingår i: Applied Network Science. - 2364-8228. ; 3:25
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Tidskriftsartikel (refereegranskat)abstract
- Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.
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