Language agnostic meme-filtering for hashtag-based social network analysis

Dimitrios Kotsakos, Panos Sakkos, Ioannis Katakis, Dimitrios Gunopulos

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Users in social networks utilize hashtags for a variety of reasons. In many cases, hashtags serve retrieval purposes by labeling the content they accompany. More often than not, hashtags are used to promote content, ideas, or conversations producing viral memes. This paper addresses a specific case of hashtag classification: meme-filtering. We argue that hashtags that are correlated with memes may hinder many valuable social media algorithms like trend detection and event identification. We propose and evaluate a set of language-agnostic features that aid the separation of these two classes: meme-hashtags and event-hashtags. The proposed approach is evaluated on two large datasets of Twitter messages written in English and German. A proof-of-concept application of the meme-filtering approach to the problem of event detection is presented.

Original languageEnglish
Article number28
Pages (from-to)1-14
Number of pages14
JournalSocial Network Analysis and Mining
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015

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