Finding interest groups from Twitter lists
| Autor: | Céline Robardet, Jean Savinien, Mohamed Benabdelkrim |
|---|---|
| Přispěvatelé: | Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Social network Computer science business.industry Local community detection 020207 software engineering 02 engineering and technology Field (computer science) Twitter list analysis [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] World Wide Web [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing 020204 information systems [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) business pattern mining |
| Zdroj: | SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, Mar 2020, Brno Czech Republic, France. pp.1885-1887, ⟨10.1145/3341105.3374077⟩ SAC |
| DOI: | 10.1145/3341105.3374077⟩ |
| Popis: | International audience; Twitter lists enable users of the social network to organize people they follow into groups of interest (e.g. politicians or journalists they like, favorite artists or athletes, authoritative figures in a given field, and so on). For the analyst, lists are a means of access to the structure of interactions between Twitter users and can be used to identify main actors of a field of interest. In this work, we introduce a methodology for constructing an edge-attributed multilayer network of Twitter users based on their membership to Twitter lists. We propose and validate a new approach that identifies local communities of users and their common interests from the constructed graph. We provide evidences that our method performs in a better way than global community detection approaches, and faster with as good results as competitive local methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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