• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 20, Pages: 2056-2063

Original Article

Transformative User Credibility Assessment on Twitter: A RNN based Heuristic Approach

Received Date:08 December 2023, Accepted Date:22 April 2024, Published Date:14 May 2024


Objectives: To construct a comprehensive weighted multi-dimensional model to assess the impact of influence score of Twitter users, considering the credibility based on user profile, their tweets and social interactions aiming to empower users in distinguishing fake news or misinformation. Methods: The credibility evaluation is formulated based on text analysis, user account attributes, and user social engagement. We've gathered around 100,000 tweets from 100 users using Tweepy API over a six-month duration for the purpose of evaluating credibility. The collected tweets spanned diverse professions namely politics, entertainment, business, science, sports, and trending topics. We chose to utilize a self-devised deep active learning model to classify and label the unlabelled data instead of engaging in time-consuming human annotation for the tweets we gathered. Findings: The obtained accuracy for influence score evaluation for Recurrent Neural Network, Random Forest, Naïve Bayes, Decision Tree, and Support Vector Machine are 89.03%, 79.10%, 81.59%, 73.06% and 79.45% respectively. Upon reviewing and analysing the outcomes, RNN surpassed all other models achieving an exceptional accuracy of 89.03%. Novelty: Employing a weighted multi-dimensional framework, it systematically evaluates the influence score by considering the credibility of both users and tweets within the context of Twitter. Weighted features are instrumental in capturing the relative importance of different elements, leading to a more refined and context-aware decision-making process. In contrast to earlier research, which predominantly centred on the credibility of individual tweets, our research work shifts the focus to a broader perspective, encompassing the credibility of users, their tweets and their overall social influence. By incorporating user influence score, the framework not only empower users in discerning fake news or mis-information but also elevates their ability to gauge the reliability of information, offering a nuanced approach to news credibility analysis.

Keywords: Active Learning, Credibility Score, User Influence, Twitter, Machine Learning, Recurrent Neural Network


  1. Yang KC, Ferrara E, Menczer F. Botometer 101: social bot practicum for computational social scientists. Journal of Computational Social Science. 2022;5(2):1511–1528. Available from: https://dx.doi.org/10.1007/s42001-022-00177-5
  2. Al-Yazidi S, Berri J, Al-Qurishi M, Al-Alrubaian M. Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review. IEEE Access. 2020;8:105824–105851. Available from: https://dx.doi.org/10.1109/access.2020.2999033
  3. Diaz-Garcia JA, Ruiz MD, Martin-Bautista MJ. NOFACE: A new framework for irrelevant content filtering in social media according to credibility and expertise. Expert Systems with Applications. 2022;208:1–15. Available from: https://dx.doi.org/10.1016/j.eswa.2022.118063
  4. Ahmad F, Rizvi SAM. Identification of user’s credibility on twitter social networks. Indonesian Journal of Electrical Engineering and Computer Science. 2021;24(1):554–563. Available from: https://dx.doi.org/10.11591/ijeecs.v24.i1.pp554-563
  5. Sitaula N, Mohan CK, Grygiel J, Zhou X, Zafarani R. Credibility-Based Fake News Detection. In: Disinformation, Misinformation, and Fake News in Social Media, Lecture Notes in Social Networks. (pp. 163-182) 2020.
  6. Cardinale Y, Dongo I, Robayo G, Cabeza D, Aguilera A, Medina S. T-CREo: A Twitter Credibility Analysis Framework. IEEE Access. 2021;9:32498–32516. Available from: https://dx.doi.org/10.1109/access.2021.3060623
  7. Khan T, Michalas A. Seeing and Believing: Evaluating the Trustworthiness of Twitter Users. IEEE Access. 2021;9:110505–110516. Available from: https://dx.doi.org/10.1109/access.2021.3098470
  8. Abu-Salih B, Wongthongtham P, Chan KY, Zhu D. CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor. Journal of Information Science. 2018;45(2):259–280. Available from: https://dx.doi.org/10.1177/0165551518790424
  9. Azer M, Taha M, Zayed HH, Gadallah M. Credibility Detection on Twitter News Using Machine Learning Approach. International Journal of Intelligent Systems and Applications. 2021;13(3):1–10. Available from: https://dx.doi.org/10.5815/ijisa.2021.03.01
  10. Iftene A, Gîfu D, Miron AR, Dudu MS. A real-time system for credibility on twitter. In: Proceedings of the Twelfth Language Resources and Evaluation Conference. (pp. 6166-6173) European Language Resources Association. 2020.
  11. Silva THMD. A Network Analysis Based Credibility Ranking Model to Combat Misinformation on Twitter. University of Colombo School of Computing thesis


© 2024 Nair & Pareek.  This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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