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Tweet Credibility Ranker: A Credibility Features’ Fusion Model

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Abstract

Misinformation on social media has emerged as a modern weapon of warfare, disrupting societal peace, trust, justice, and democracy. It is quite challenging to address the issue of information credibility for microblogs. It becomes more challenging when the authenticity of the poster is hidden. The concept of information credibility has multi-perspectives. There are many necessary aspects of information credibility which must be considered for effective credibility assessment. It is observed that some important aspects of credibility are not considered in existing studies. The complete credibility assessment solution needs a comprehensive and diverse set of features for such complex identification. Therefore, these features are identified and proposed by exploring the related research studies consisting of the necessary credibility aspects. These features consist of diverse levels provided by microblogs. These levels include the post, poster, poster’s social network, and actual information propagation network. An exploratory study is also conducted to propose the best credibility features that are used in the proposed solution. The attempt is made for a hybrid features fusion model which combines feature-based or machine learning and graph-based approaches. It is a lightweight, high-performing, non-latent features model to avoid their drawbacks. It assesses the levels of credibility of the concerned post. It is designed for high-impact applications to combat low-credibility content during elections, crises, and other critical scenarios. The model is executed over a publicly available dataset extended for credibility assessment. The model provides good results with 95.6% accuracy by XGBoost using platinum features. The performance of the proposed model is compared with state-of-the-art that produced much-appreciating results.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Performed the role of additional data fetching: Maad Saifuddin, Syed Ahmed Ali Naqvi, Faraz Kayani, and Ghayas-Uddin Adam at Department of Computer Science, DHA Suffa University, Karachi, Pakistan.

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    Khubaib Ahmed Qureshi & Rauf Ahmed Shams Malick

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Appendices

Appendix A. Proposed User-Based Features

Table 14 Proposed user-based features: explored and identified from related and supported research studies
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Appendix B. Proposed Post-Based Features

Table 15 Proposed post-based features: explored and identified from related and supported research studies
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Appendix C. Guided Data Tagging GUI

The GUI of the application used to rank the tweets using the guided data tagging method is shown as follows in Fig. 11.

Fig. 11
figure 11

Guided data tagging: credibility ranking GUI

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Table 16 Agreement (O) and Kappa (K) values between experts and ground truths
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Appendix D: Discriminating Features-eCDF Plots

The most discriminating feature distributions are plotted using empirical cumulative distribution function (eCDF). It could be observed that all features are highly discriminating. Only 20 features are plotted out of 46 features (see Fig. 12).

Fig. 12
figure 12

Feature analysis: few features are plotted using eCDF plots that show that these features are highly discriminating

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Appendix E. Classification Models Results

Table 17 Multi-class classification models complete results
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Table 18 Binary-class classification models complete results
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Qureshi, K.A., Malick, R.A.S. Tweet Credibility Ranker: A Credibility Features’ Fusion Model. Cogn Comput 17, 56 (2025). https://doi.org/10.1007/s12559-025-10413-5

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