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Detection of automated behavior on Twitter through approximate entropy and sample entropy

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Abstract

Twitter is an Online Social Network (OSN). It is a significant forum for public expression and building relationships. By 2020, Twitter has reached nearly 353.1 million active users per month. However, a considerable number of online user accounts can be automated profiles. It is predicted that roughly 52 million profiles on Twitter are bots. Some bots perform positive operations like publishing news, scientific articles, and support emergencies. However, there also exist some bots that deceive genuine users by sharing spurious content or distributing malware. Henceforth, discovery of suspicious accounts is mandatory to ensure a safe Twitter environment. This paper has proposed novel approaches to identify bots by determining the randomness and regularity present in the temporal tweet attribute of the user. The real-time tweets posted by individual Twitter profiles are collected and the number of tweets posted by the user over a sampling period is extracted as an activity signal. Later, the degree of regularity present in the activity signals is measured through the lens of entropy. In this work, the probabilistic concepts, Approximate Entropy, and Sample Entropy are utilized to quantify the global degree of regularity in the signal. Accounts with entropy values less than the fixed threshold are labeled as bots. Thus, the nature of the Twitter profile (bot or human) can be determined. Our technique yields an F1 score of 0.8759 and 0.8349 for Approximate Entropy and Sample Entropy, respectively. Point-biserial correlation is employed to establish the association between the entropy values and the class of Twitter users.

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Funding

This work has been supported by Research Grant No.SPG/2020/000594 under the SERB POWER grant scheme, Science and Engineering Research Board, Government of India., to Akila Venkatesan, Pondicherry Engineering College, India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pondicherry Engineering College, Pondicherry, India

    Rosario Gilmary & Akila Venkatesan

  2. Department of Information Technology, Pondicherry Engineering College, Pondicherry, India

    Govindasamy Vaiyapuri

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  1. Rosario Gilmary
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  2. Akila Venkatesan
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  3. Govindasamy Vaiyapuri
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Correspondence to Rosario Gilmary.

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Gilmary, R., Venkatesan, A. & Vaiyapuri, G. Detection of automated behavior on Twitter through approximate entropy and sample entropy. Pers Ubiquit Comput 27, 91–105 (2023). https://doi.org/10.1007/s00779-021-01647-9

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