PlatformGovernance as Decision Support: A Governance RiskIndex for Twitter/X During the 2024 U.S. Presidential Campaign

Authors

  • Abdullah Önden Author

Abstract

Online platforms have emerged as essential infrastructure for democratic deliberation, crisis communication, and public healthmessaging.Nonetheless, governance of these spaces remains largely retrospective and principle-based, without concrete evaluative criteria.This article operationalizes six key principle-based elements of platform governance into concrete clause components with quantifiable triggers,to support proactive application. We develop a composite Governance RiskIndex (GRI), integrating empirically defined decision thresholds for four risk components: dispersion,drift, inequality, and toxicity. We estimate toxicity levels from a stratified subsample of the dataset labeled using the Perspective API, aggregated to the daily level. Our results show that toxicity is positively correlated with engagement (r= 0.52,p< .001), as replicated in our data (r= 0.49,p< .001),in line with algorithmic amplification dynamics thatplatform governance must account for. We further find that decision thresholds differ meaningfully between clause components and risk components. A contextual benchmark against 2015–2016 multi-platform baselines reveals that the 2024 Twitter/X environment exhibits substantially different statistical properties in engagement dispersion and toxicity prevalence, highlighting the need for context-dependent calibration of platform governance thresholds. Over a 30-day out-of-sample holdout period, validated exclusively against independently verified external events, the GRI classified governance-event days under retrospective validation with 85.1% accuracy. Under a supplementary labeling scheme that incorporates platform-internal anomaly criteria, accuracy reaches 90.0%, representing an improvement of about 13 percentage points over single-metric baselines, highlighting the advantages of multivariate, multi-faceted operationalization in proactive platform governance.

Author Biography

  • Abdullah Önden

    Department of Computer Engineering, Istanbul University, Faculty of Computer and Information Technologies,Istanbul, Türkiye

References

https://doi.org/10.3390/systems14020216.

[23]Suoniemi, S., Meyer-Waarden, L., Munzel, A., Zablah, A. R., & Straub, D. (2020). Big data and firm performance: The

roles of market-directed capabilities and businessstrategy.Information & Management, 57(7), 103365.

https://doi.org/10.1016/j.im.2020.103365.

[24]Shiells, K., Di Cara, N., Skatova, A., Davis, O. S., Haworth, C. M., Skinner, A. L., ... & Boyd, A. (2022). Participant

acceptability of digital footprint data collectionstrategies: an exemplar approach to participant engagement and involvement in the ALSPAC birth cohort study.International Journal of Population Data Science, 5(3), 1728.https://doi.org/10.23889/ijpds.v5i3.1728.

[25]Jayasuriya, D. D., Ayaz, M., & Williams, M. (2023).The use of digital footprints in the US mortgage market.

Accounting & Finance, 63(1), 353-401.https://doi.org/10.1111/acfi.12946.

[26]Faruq, M. O. (2024). Vendor risk management in cloud-centric architectures: A systematic review of soc2, Fedramp,

and ISO 27001 practices.International Journal of Business and Economics Insights, 4(1), 01-32.

https://doi.org/10.63125/j64vb122.

[27]Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2025). Privacy concerns in social media UGCcommunities:

Understanding user behavior sentiments in complex networks: JR Saura et al.Information Systems and e-Business Management, 23(1), 125-145.https://doi.org/10.1007/s10257-023-00631-5.

[28]Iwan-Sojka, D. (2025). The inclusive data governance models for algorithms–a dream of the already convinced or a

realistic way of action?.Information & Communications Technology Law, 34(1), 3-16.

https://doi.org/10.1080/13600834.2024.2406668.

[29]Aldhi, I. F., Suhariadi, F., Rahmawati, E., Supriharyanti, E., Hardaningtyas, D., Sugiarti, R., & Abbas, A. (2025).

Bridging digital gaps in smart city governance: themediating role of managerial digital readiness andthe moderating role of digital leadership.Smart Cities, 8(4), 117.https://doi.org/10.3390/smartcities8040117.

[30]Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions.Advances in Neural

Information Processing Systems, 30.

[31]Önden, A., Kara, K., Önden, İ., Yalçın, G. C., Simic, V., & Pamucar, D. (2024). Exploring the adoptionof the metaverse

and chat generative pre-trained transformer: A single-valued neutrosophic Dombi Bonferroni-based method for the selection of software development strategies.Engineering Applications of Artificial Intelligence, 133, 108378.https://doi.org/10.1016/j.engappai.2024.108378.

[32]Cunneen, M., AnandFinn, R., Friel, R., Tennent, P.,& Brandt, S. (2025). From bones to bytes: anticipating and

addressing the governance challenges of human digital remains and posthumous digital human twins.AI & Society.https://doi.org/10.1007/s00146-025-02514-4.

[33]Jonnala, N. S., Ram Teja, A. V. S., Rajeswari, S. R., Jakeer, S., Dheeraj, A., Bansal, S., ... & Al-Mugren, K. S. (2025).

Leveraging hybrid model for accurate sentiment analysis of Twitter data.Scientific Reports, 15(1), 24438.https://doi.org/10.1038/s41598-025-09794-2.

[34]Torgo, L., & Moniz, N. (2018). News popularity in multiple social media platforms [Dataset].UCI Machine Learning

Repository.https://doi.org/10.24432/C5H029.

[35]Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text.

InProceedings of the International AAAI Conference onWeb and Social Media, pp. 216-225.

https://doi.org/10.1609/icwsm.v8i1.14550.

[36]Devlin, J., Chang, M. W., Lee, K., & Toutanova, K.(2019). Bert: Pre-training of deep bidirectional transformers for

language understanding. InProceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171-4186.https://doi.org/10.18653/v1/N19-1423.

Published

2026-07-21

How to Cite

(1)
PlatformGovernance As Decision Support: A Governance RiskIndex for Twitter X During the 2024 U.S. Presidential Campaign. SEMS 2026, 4 (1), 55-76.