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Differences in the Toxic Language of Cross-Platform Communities
| dc.contributor.author | Singh, Ashwini Kumar | |
| dc.contributor.author | Ghafouri, Vahid | |
| dc.contributor.author | Such, Jose | |
| dc.contributor.author | Suarez-Tangil, Guillermo | |
| dc.date.accessioned | 2024-01-12T09:17:13Z | |
| dc.date.available | 2024-01-12T09:17:13Z | |
| dc.date.issued | 2024-06-03 | |
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earthquake sequence on Twitter and Reddit}, volume = {12}, journal = {Scientific Reports}, doi = {10.1038/s41598-022-05359-9} } @article{Hall2018, author = {Margeret Hall and Athanasios Mazarakis and Martin Chorley and Simon Caton}, title = {Editorial of the Special Issue on Following User Pathways: Key Contributions and Future Directions in Cross-Platform Social Media Research}, journal = {International Journal of Human–Computer Interaction}, volume = {34}, number = {10}, pages = {895-912}, year = {2018}, publisher = {Taylor & Francis}, doi = {10.1080/10447318.2018.1471575}, URL = { https://doi.org/10.1080/10447318.2018.1471575 }, eprint = { https://doi.org/10.1080/10447318.2018.1471575 } , abstract = { ABSTRACTSocial media and the resulting tidal wave of the available data have changed the ways and methods researchers analyze communities at scale. But the full potential for social scientists (and others) is not yet achieved. Despite the popularity of social media analysis in the past decade, few researchers invest in cross-platform analyses. This is a major oversight as a majority of online social media users have multiple social media accounts. Missing are the models and tools necessary to undertake analysis at scale across multiple platforms. Especially promising in support of cross-platform analysis is the mixed-method approach (e.g., qualitative and quantitative methods) in order to better understand how users and society interact online. This special issue “Following User Pathways” addresses methodological, analytical, conceptual, and technological challenges and opportunities of cross-platform analysis in social media ecosystems. } } @article{van2019echo, title={The echo chamber of anti-vaccination conspiracies: mechanisms of radicalization on Facebook and Reddit}, author={Van Raemdonck, Nathalie}, journal={Institute for Policy, Advocacy and Governance (IPAG) Knowledge Series, Forthcoming}, year={2019} } @INPROCEEDINGS{Vicario2017, author={Vicario, Michela Del and Gaito, Sabrina and Quattrociocchi, Walter and Zignani, Matteo and Zollo, Fabiana}, booktitle={2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, title={News Consumption during the Italian Referendum: A Cross-Platform Analysis on Facebook and Twitter}, year={2017}, volume={}, number={}, pages={648-657}, doi={10.1109/DSAA.2017.33} } @article{Yang2021, author = {Kai-Cheng Yang and Francesco Pierri and Pik-Mai Hui and David Axelrod and Christopher Torres-Lugo and John Bryden and Filippo Menczer}, title ={The COVID-19 Infodemic: Twitter versus Facebook}, journal = {Big Data \& Society}, volume = {8}, number = {1}, pages = {20539517211013861}, year = {2021}, doi = {10.1177/20539517211013861}, URL = { https://doi.org/10.1177/20539517211013861 }, eprint = { https://doi.org/10.1177/20539517211013861 } , abstract = { The global spread of the novel coronavirus is affected by the spread of related misinformation—the so-called COVID-19 Infodemic—that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation “superspreaders” are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level solutions in addition to mitigation strategies within the platforms. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems. } } @inproceedings{tahmasbi2021go, title={``Go eat a bat, Chang!'': On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19}, author={Tahmasbi, Fatemeh and Schild, Leonard and Ling, Chen and Blackburn, Jeremy and Stringhini, Gianluca and Zhang, Yang and Zannettou, Savvas}, booktitle={Proceedings of the web conference 2021}, pages={1122--1133}, year={2021} } @inproceedings{ribeiro2021evolution, title={The evolution of the manosphere across the Web}, author={Ribeiro, Manoel Horta and Blackburn, Jeremy and Bradlyn, Barry and De Cristofaro, Emiliano and Stringhini, Gianluca and Long, Summer and Greenberg, Stephanie and Zannettou, Savvas}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={15}, pages={196--207}, year={2021} } @inproceedings{zannettou2018origins, title={On the origins of memes by means of fringe web communities}, author={Zannettou, Savvas and Caulfield, Tristan and Blackburn, Jeremy and De Cristofaro, Emiliano and Sirivianos, Michael and Stringhini, Gianluca and Suarez-Tangil, Guillermo}, booktitle={Proceedings of the Internet Measurement Conference 2018}, pages={188--202}, year={2018} } @inproceedings{ali2021understanding, title={Understanding the effect of deplatforming on social networks}, author={Ali, Shiza and Saeed, Mohammad Hammas and Aldreabi, Esraa and Blackburn, Jeremy and De Cristofaro, Emiliano and Zannettou, Savvas and Stringhini, Gianluca}, booktitle={13th ACM Web Science Conference 2021}, pages={187--195}, year={2021} } @article{horta2021platform, title={Do platform migrations compromise content moderation? evidence from r/the\_donald and r/incels}, author={Horta Ribeiro, Manoel and Jhaver, Shagun and Zannettou, Savvas and Blackburn, Jeremy and Stringhini, Gianluca and De Cristofaro, Emiliano and West, Robert}, journal={Proceedings of the ACM on Human-Computer Interaction}, volume={5}, number={CSCW2}, pages={1--24}, year={2021}, publisher={ACM New York, NY, USA} } @inproceedings{evans2007differential, title={Differential testing: a new approach to change detection}, author={Evans, Robert B and Savoia, Alberto}, booktitle={The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering: Companion Papers}, pages={549--552}, year={2007} } @misc{he2023prompt, title={You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content}, author={Xinlei He and Savvas Zannettou and Yun Shen and Yang Zhang}, year={2023}, eprint={2308.05596}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{Si2022chat, author = {Si, Wai Man and Backes, Michael and Blackburn, Jeremy and De Cristofaro, Emiliano and Stringhini, Gianluca and Zannettou, Savvas and Zhang, Yang}, title = {Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots}, year = {2022}, isbn = {9781450394505}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3548606.3560599}, doi = {10.1145/3548606.3560599}, abstract = {Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety.}, booktitle = {Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security}, pages = {2659–2673}, numpages = {15}, keywords = {trustworthy machine learning, dialogue system, online toxicity}, location = {Los Angeles, CA, USA}, series = {CCS '22} } @article{ali2023instagram, author = {Ali, Shiza and Razi, Afsaneh and Kim, Seunghyun and Alsoubai, Ashwaq and Ling, Chen and De Choudhury, Munmun and Wisniewski, Pamela J. and Stringhini, Gianluca}, title = {Getting Meta: A Multimodal Approach for Detecting Unsafe Conversations within Instagram Direct Messages of Youth}, year = {2023}, issue_date = {April 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {7}, number = {CSCW1}, url = {https://doi.org/10.1145/3579608}, doi = {10.1145/3579608}, abstract = {Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption for private conversations will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = {apr}, articleno = {132}, numpages = {30}, keywords = {social media, machine learning, online risk detection, instagram, ensemble models, end-to-end encryption} } | es |
| dc.identifier.uri | https://hdl.handle.net/20.500.12761/1776 | |
| dc.description.abstract | Cross-platform communities are social media communities that have a presence on multiple online platforms. One active community on both Reddit and Discord is dankmemes. Our study aims to examine differences in harmful language usage across different platforms in a community. We scrape 15 communities that are active on both Reddit and Discord. We then identify and compare differences in type and level of toxicity, in the topics of the harmful discourse, in the temporal evolution of toxicity and its attribution to users, and in the moderation strategies communities across platforms. Our results show that most communities exhibit differences in toxicity depending on the platform. We see that toxicity is rooted in the different subcultures as well as in the way in which the platforms operate and their administrators moderate content. However, we note that in general terms Discord is significantly more toxic than Reddit. We offer a detailed analysis of the topics and types of communities in which this happens and why, which will help moderators and policymakers shape their strategies to mitigate the harm on the Web. In particular, we propose practical and effective strategies that Discord can implement to improve their platform moderation. | es |
| dc.description.sponsorship | UK's Research centre on Privacy, Harm Reduction & Adversarial Influence online | es |
| dc.description.sponsorship | Spanish Ministry of Science and Innovation | es |
| dc.description.sponsorship | ESF Investing in your future | es |
| dc.description.sponsorship | European Union-NextGenerationEU | es |
| dc.language.iso | eng | es |
| dc.title | Differences in the Toxic Language of Cross-Platform Communities | es |
| dc.type | conference object | es |
| dc.conference.date | 3-6 June 2024 | es |
| dc.conference.place | Buffalo, New York, USA | es |
| dc.conference.title | International AAAI Conference on Web and Social Media | * |
| dc.event.type | conference | es |
| dc.pres.type | paper | es |
| dc.type.hasVersion | AO | es |
| dc.rights.accessRights | open access | es |
| dc.relation.projectID | EP/V011189/1 | es |
| dc.relation.projectID | MCIN/AEI/10.13039/501100011033 | es |
| dc.relation.projectID | RYC-2020-029401-I | es |
| dc.relation.projectID | TED2021-132900A-I00 | es |
| dc.relation.projectName | REPHRAIN | es |
| dc.relation.projectName | COMET | es |
| dc.relation.projectName | ESF Investing in your future | es |
| dc.relation.projectName | 2019 Ramon y Cajal fellow | es |
| dc.subject.keyword | toxicity | es |
| dc.subject.keyword | NLP | es |
| dc.subject.keyword | social networks | es |
| dc.subject.keyword | Discord | es |
| dc.subject.keyword | es | |
| dc.subject.keyword | cross-platform | es |
| dc.description.refereed | TRUE | es |
| dc.description.status | inpress | es |
