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.
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Obviously, there are exception to this rule. In our times, information about earthquakes appears on social media first. However, this is still related to a real-world event.
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Acknowledgments
The authors would like to thank the data annotators. This work has been co-financed by EU and Greek National funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Programs: Heraclitus II fellowship, THALIS - GeomComp, THALIS - DISFER, ARISTEIA - MMD,” and the EU funded project INSIGHT.
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Kotsakos, D., Sakkos, P., Katakis, I. et al. Language agnostic meme-filtering for hashtag-based social network analysis. Soc. Netw. Anal. Min. 5, 28 (2015). https://doi.org/10.1007/s13278-015-0271-3
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DOI: https://doi.org/10.1007/s13278-015-0271-3