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
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
ICWSM
A multi-platform collection of social media posts about the 2022 US midterm elections
Rachith Aiyappa, Matthew R DeVerna, Manita Pote, and 8 more authors
In Proceedings of the International AAAI Conference on Web and Social Media, Jun 2023
@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 and others},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
ICWSM
Uncovering coordinated networks on social media: methods and case studies
Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, and 3 more authors
In Proceedings of the International AAAI Conference on Web and Social Media, Jun 2021
@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}}
ICWSM
CoVaxxy: A collection of English-language Twitter posts about COVID-19 vaccines
Matthew R DeVerna, Francesco Pierri, Bao Tran Truong, and 7 more authors
In Proceedings of the International AAAI Conference on Web and Social Media, Jun 2021
@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}}
