publications | Bao T. Truong

2026

Community Notes are Vulnerable to Rater Bias and Manipulation

Bao Tran Truong, Siqi Wu, Alessandro Flammini, Filippo Menczer, and Alexander J. Stewart . arXiv preprint, 2026.

Abstract

Social media platforms increasingly rely on crowdsourced moderation systems like Community Notes to combat misinformation at scale. However, these systems face challenges from rater bias and potential manipulation, which may undermine their effectiveness. Here we systematically evaluate the Community Notes algorithm using simulated data that models realistic rater and note behaviors, quantifying error rates in publishing helpful versus unhelpful notes. We find that the algorithm suppresses a substantial fraction of genuinely helpful notes and is highly sensitive to rater biases, including polarization and in-group preferences. Moreover, a small minority (5–20%) of bad raters can strategically suppress targeted helpful notes, effectively censoring reliable information. These findings suggest that while community-driven moderation may offer scalability, its vulnerability to bias and manipulation raises concerns about reliability and trustworthiness, highlighting the need for improved mechanisms to safeguard the integrity of crowdsourced fact-checking.
Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts

Ozgur Can Seckin, Bao Tran Truong, Filippo Menczer, and Alessandro Flammini . ICWSM 2026, forthcoming, 2026.

Abstract

Bridging content that brings together individuals with opposing viewpoints on social media remains elusive, overshadowed by echo chambers and toxic exchanges. We propose that algorithmic curation could surface such content by considering constructive conflicts as a foundational criterion. We operationalize this criterion through controversiality to identify challenging dialogues and toxicity resilience to capture respectful conversations. We develop high-accuracy models to capture these dimensions. Analyses based on these models demonstrate that assessing resilience to toxic responses is not the same as identifying low-toxicity posts. We also find that political posts are often controversial and tend to attract more toxic responses. However, some posts, even the political ones, are resilient to toxicity despite being highly controversial, potentially sparking civil engagement. Toxicity resilient posts tend to use politeness cues, such as showing gratitude and hedging. These findings suggest the potential for framing the tone of posts to encourage constructive political discussions.

2025

The Rise of Bluesky

Ozgur Can Seckin, Filipi Nascimento Silva, Bao Tran Truong, Sangyeon Kim, Fan Huang, Nick Liu, Alessandro Flammini, and Filippo Menczer . arXiv preprint, 2025.

Abstract

This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.
Delayed takedown of illegal content on social media makes moderation ineffective

Bao Tran Truong, Sangyeon Kim, Erfan Samieyan Sahneh, Gianluca Nogara, Enrico Verdolotti, Natascha Just, Florian Saurwein, Luca Luceri, Silvia Giordano, and Filippo Menczer . arXiv preprint, 2025.

Abstract

Illegal content on social media poses significant societal harm and necessitates timely removal. However, the impact of the speed of content removal on prevalence, reach, and exposure to illegal content remains underexplored. This study examines the relationship with a systematic analysis of takedown delays using data from the EU Digital Services Act Transparency Database, covering five major platforms over a one-year period. We find substantial variation in takedown delay, with some content remaining online for weeks or even months. To evaluate how these delays affect the prevalence and reach of illegal content and exposure to it, we develop an agent-based model and calibrate it to empirical data. We simulate illegal content diffusion, revealing that rapid takedown (within hours) significantly reduces prevalence, reach, and exposure to illegal content, while longer delays fail to reduce its spread. Though the effect of delay may seem intuitive, our simulations quantify exactly how takedown speed shapes the spread of illegal content. Building on these results, we point to the benefits of faster content removal to effectively curb the spread of illegal content, while also considering the limitations of strict enforcement policies.
Federating Governance: How Community Rules Scale with Mastodon Servers

Rasika Muralidharan, Yong-yeol Ahn, and Bao Tran Truong . working paper, 2025.

2024

Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics

Bao Tran Truong, Xiaodan Lou, Alessandro Flammini, and Filippo Menczer . PNAS Nexus, 2024.

Abstract Bib Code

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
```
@article{truong2023vulnerabilities,
  author = {Truong, Bao Tran and Lou, Xiaodan and Flammini, Alessandro and Menczer, Filippo},
  title = {Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics},
  journal = {PNAS Nexus},
  volume = {3},
  number = {7},
  pages = {pgae258},
  year = {2024},
  month = jun,
  issn = {2752-6542},
  doi = {10.1093/pnasnexus/pgae258},
  url = {https://doi.org/10.1093/pnasnexus/pgae258},
}
```
Account credibility inference based on news-sharing networks

Bao Tran Truong, Oliver Melbourne Allen, and Filippo Menczer . EPJ Data Science, 2024.

Abstract Bib Code Paper

The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account’s trust in other accounts, and the bipartite account-source network, capturing an account’s trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other’s content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.
```
@article{truong2024account,
  title = {Account credibility inference based on news-sharing networks},
  author = {Truong, Bao Tran and Allen, Oliver Melbourne and Menczer, Filippo},
  journal = {EPJ Data Science},
  volume = {13},
  number = {1},
  pages = {1--19},
  year = {2024},
  publisher = {SpringerOpen},
  url = {https://rdcu.be/dxt25},
}
```

2023

A multi-platform collection of social media posts about the 2022 US midterm elections

Rachith Aiyappa, Matthew R DeVerna, Manita Pote, Bao Tran Truong, Wanying Zhao, David Axelrod, Aria Pessianzadeh, Zoher Kachwala, Munjung Kim, and Ozgur Can Seckin . In Proceedings of the International AAAI Conference on Web and Social Media, 2023.

Bib Code Paper

@inproceedings{aiyappa2023multi,
  title = {A multi-platform collection of social media posts about the 2022 US midterm elections},
  author = {Aiyappa, Rachith and DeVerna, Matthew R and Pote, Manita and Truong, Bao Tran and Zhao, Wanying and Axelrod, David and Pessianzadeh, Aria and Kachwala, Zoher and Kim, Munjung and Seckin, Ozgur Can},
  booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {17},
  pages = {981--989},
  year = {2023},
  doi = {10.1609/icwsm.v17i1.22205},
  url = {https://ojs.aaai.org/index.php/ICWSM/article/view/22205}
}

2021

Uncovering coordinated networks on social media: methods and case studies

Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong, Alessandro Flammini, and Filippo Menczer . In Proceedings of the International AAAI Conference on Web and Social Media, 2021.

Bib Code

@inproceedings{pacheco2021uncovering,
  title = {Uncovering coordinated networks on social media: methods and case studies},
  author = {Pacheco, Diogo and Hui, Pik-Mai and Torres-Lugo, Christopher and Truong, Bao Tran and Flammini, Alessandro and Menczer, Filippo},
  booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {15},
  pages = {455--466},
  year = {2021},
  url = {https://ojs.aaai.org/index.php/ICWSM/article/view/18075}
}

CoVaxxy: A collection of English-language Twitter posts about COVID-19 vaccines

Matthew R DeVerna, Francesco Pierri, Bao Tran Truong, John Bollenbacher, David Axelrod, Niklas Loynes, Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer, and John Bryden . In Proceedings of the International AAAI Conference on Web and Social Media, 2021.

Bib Code Paper

@inproceedings{deverna2021covaxxy,
  title = {CoVaxxy: A collection of English-language Twitter posts about COVID-19 vaccines},
  author = {DeVerna, Matthew R and Pierri, Francesco and Truong, Bao Tran and Bollenbacher, John and Axelrod, David and Loynes, Niklas and Torres-Lugo, Christopher and Yang, Kai-Cheng and Menczer, Filippo and Bryden, John},
  booktitle = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {15},
  pages = {992--999},
  year = {2021},
  url = {https://ojs.aaai.org/index.php/ICWSM/article/view/18122}
}