Detecting Real-World Influence Through Twitter
| العنوان: | Detecting Real-World Influence Through Twitter |
|---|---|
| المؤلفون: | Nicolas Dugué, Jean-Valère Cossu, Vincent Labatut |
| المساهمون: | Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), ANR-12-CORD-0002,ImagiWeb,Images sur le Web : analyse de la dynamique des images sur le Web 2.0.(2012) |
| المصدر: | 2nd European Network Intelligence Conference (ENIC) 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Sweden. pp.83-90, ⟨10.1109/ENIC.2015.20⟩ |
| بيانات النشر: | HAL CCSD, 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Offline influence, Computer Science - Artificial Intelligence, Computer science, Twitter, Context (language use), 02 engineering and technology, 01 natural sciences, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Ranking (information retrieval), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], RepLab, 020204 information systems, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 010306 general physics, Social network analysis, Natural Language Processing, Social and Information Networks (cs.SI), Information retrieval, Rank (computer programming), [INFO.INFO-WB]Computer Science [cs]/Web, Computer Science - Social and Information Networks, Classification, Influencer marketing, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Artificial Intelligence (cs.AI), Ranking, Online influence, Social Network Analysis |
| الوصف: | International audience; In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods. |
| اللغة: | English |
| DOI: | 10.1109/ENIC.2015.20⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60428c609dfa967294c399e2cefc29de https://hal.archives-ouvertes.fr/hal-01164453v2/document |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....60428c609dfa967294c399e2cefc29de |
| قاعدة البيانات: | OpenAIRE |
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