Abstract
Social networks are useful for linking micro and macro levels of sociological theory by enabling the analysis of various forms of relationships. In social science, a taxonomy of social relationships is described as a function of closeness among users. The closer the users are, the more cohesive and trustworthy. Identifying dyadic ties, pairs of fully connected users, on Twitter is challenging due to the flexible and eccentric underlying connection patterns. The ability to follow anyone results in many unidirectional connections between socially disconnected users and ultimately affects clustering users and, in turn, the veracity of online content. Major challenges towards effective user clustering are the low number of dyads and efficient methods to identify more. In this study, we query over 17M verified and unverified Twitter user accounts and retrieve dyadic ties. In the collected data, \(55\%\) and \(21\%\) of unverified and verified profiles, respectively, participate in dyadic ties. We describe the importance of dyads in the detection of cohesive user groups and how they may be used to validate trustworthiness. We demonstrate how identifying and using dyadic ties will improve Twitter analysis, in the future. Finally, we develop a deep learning model for dyad prediction.
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Notes
- 1.
See github.com/ijdutse/dyads_in_Twitter for details about the data of the study.
- 2.
Dyadic tie, pairwise or binary relations are used interchangeable in this work.
- 3.
These are genuine users devoid of spammers or social bots collected based on the SPD filtering technique [8].
- 4.
We utilise Glove word embeddings [14], pre-trained on tweet collections.
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Acknowledgements
This research work is part of the CROSSMINER Project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 732223.
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Inuwa-Dutse, I., Liptrott, M., Korkontzelos, Y. (2019). Analysis and Prediction of Dyads in Twitter. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_25
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