Abstract
Online social networks have gone mainstream: millions of users have come to rely on the wide range of services provided by social networks. However, the ease use of social networks for communicating information also makes them particularly vulnerable to social spammers, i.e., ill-intentioned users whose main purpose is to degrade the information quality of social networks through the proliferation of different types of malicious data (e.g., social spam, malware downloads, and phishing) that are collectively called low-quality content. Since Twitter is also rife with low-quality content, several researchers have devised various low-quality detection strategies that inspect tweets for the existence of this kind of content. We carried out a brief literature survey of these low-quality detection strategies, examining which strategies are still applicable in the current scenario – taken into account that Twitter has undergone a lot of changes in the last few years. To gather some evidence of the usefulness of the attributes used by the low-quality detection strategies, we carried out a preliminary evaluation of these attributes.
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Notes
- 1.
As of this writing, the tweet limit is 2,400 per day. It is worth emphasizing that there is also a limit per half-hour period, so users are not able to tweet all 2,400 tweets at one time.
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Acknowledgments
This work was partially funded by the Brazilian National Institute of Science and Technology for the Web - INWeb, MASWeb, CAPES, CNPq, Finep, Fapesp and Fapemig.
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Resende, J., Durelli, V.H.S., Moraes, I., Silva, N., Dias, D.R.C., Rocha, L. (2020). An Evaluation of Low-Quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_42
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