arXiv subjects

Joseph Reagle

Publications and source records attributed to Joseph Reagle.

3 recordsLinked to original sources

arXiv2023

"There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit

Generative AI is altering how we work, learn, communicate, and participate in online communities. How might online communities be changed by generative AI? To start addressing this question, we focused on online community moderators' experiences with AI-generated content (AIGC). We performed fifteen in-depth, semi-structured interviews with moderators of Reddit communities that restrict the use of AIGC. Our study finds that rules about AIGC are motivated by concerns about content quality, social dynamics, and governance challenges. Moderators fear that, without such rules, AIGC threatens to reduce their communities' utility and social value. We find that, despite the absence of robust tools for detecting AIGC, moderators were able to somewhat limit the disruption it caused by working with their communities to clarify norms. However, moderators found enforcing AIGC restrictions challenging, as they rely on time-intensive and inaccurate detection heuristics. Our results highlight the importance of supporting community autonomy and self-determination in the face of this sudden technological change, and suggest potential design solutions that may help.

cs.CY↗

arXiv2023

Towards Effective Paraphrasing for Information Disguise

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet. Research on ID becomes important when authors' written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author's post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit 'r/AmItheAsshole' as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach.

cs.IR↗

arXiv1999

Beyond Concern: Understanding Net Users' Attitudes About Online Privacy

People are concerned about privacy, particularly on the Internet. While many studies have provided evidence of this concern, few have explored the nature of the concern in detail, especially for the online environment. With this study, we have tried to better understand the nature of online privacy concerns; we look beyond the fact that people are concerned and attempt to understand how they are concerned. We hope our results will help inform both policy decisions as well as the development of technology tools that can assist Internet users in protecting their privacy. We present results here from the analysis of 381 questionnaires completed between November 6 and November 13, 1998 by American Internet users. The sample was drawn from the FamilyPC magazine/Digital Research, Inc. Family Panel. While this is not a statistically representative sample of US Internet users, our respondents are heavy Internet users, and quite possibly lead innovators. As such, we believe that this sample is important for understanding the future Internet user population.

cs.CY↗