Abstract
The increasing availability of data from online social networks attracts researchers’ interest, who seek to build algorithms and machine learning models to analyze users’ interactions and behaviors. Different methods have been developed to detect remarkable precursors preceding events, using text mining and Machine Learning techniques on documents, or using network topology with graph patterns.
Our approach aims at analyzing social networks data, through a graphlets enumeration algorithm, to identify event precursors and to study their contribution to the event. We test the proposed method on two different types of social network data sets: real-world events (Lubrizol fire, EU law discussion), and general events (Facebook and MathOverflow). We also contextualize the results by studying the position (orbit) of important nodes in the graphlets, which are assumed as event precursors. After analysis of the results, we show that some graphlets can be considered precursors of events.
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Notes
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Implemented with the R package tseries: https://www.rdocumentation.org/packages/tseries/versions/0.1-2/topics/ccf.
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The difference between the number of tweets in Sect. 4 and the number of nodes and edges is since several tweets can produce the same interaction.
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This work is supported by the program “Investissements d’Avenir”, ISITE-BFC project (ANR contract 15-IDEX-0003), https://projet-cocktail.fr/.
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Abou Jamra, H., Savonnet, M., Leclercq, É. (2021). Detection of Event Precursors in Social Networks: A Graphlet-Based Method. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_13
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