ACM Transactions on the Web最新文献

{"title":"DeLink: An Adversarial Framework for Defending against Cross-site User Identity Linkage","authors":"Peng Zhang, Qi Zhou, Tun Lu, Hansu Gu, Ning Gu","doi":"10.1145/3643828","DOIUrl":"https://doi.org/10.1145/3643828","url":null,"abstract":"<p>Cross-site user identity linkage (UIL) aims to link the identities of the same person across different social media platforms. Social media practitioners and service providers can construct composite user portraits based on cross-site UIL, which helps understand user behavior holistically and conduct accurate recommendations and personalization. However, many social media users expect each profile to stay within the platform where it was created and thus do not want the identities of different platforms to be linked. For this problem, we first investigate the approaches people would like to use to defend against cross-site UIL and the corresponding challenges. Based on the findings, we build an adversarial framework - DeLink based on the thoughts of adversarial text generation to help people improve their social media screen names to defend against cross-site UIL. DeLink can support both Chinese and English languages and has good generalizability to the varying numbers of social media accounts and different cross-site user identity linkage models. Extensive evaluations validate DeLink’s better performance, including a higher success rate, higher efficiency, less impact on human perception, and capability to defend against different cross-site UIL models.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139689969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social Media","authors":"Lingyao Li, Lizhou Fan, Shubham Atreja, Libby Hemphill","doi":"10.1145/3643829","DOIUrl":"https://doi.org/10.1145/3643829","url":null,"abstract":"<p>Harmful textual content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to this issue is developing detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost to complete. Generative AI models have the potential to understand and detect harmful textual content. We used ChatGPT to investigate this potential and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful textual content on social media: Hateful, Offensive, and Toxic (HOT). We designed five prompts to interact with ChatGPT and conducted four experiments eliciting HOT classifications. Our results show that ChatGPT can achieve an accuracy of approximately 80% when compared to MTurker annotations. Specifically, the model displays a more consistent classification for non-HOT comments than HOT comments compared to human annotations. Our findings also suggest that ChatGPT classifications align with the provided HOT definitions. However, ChatGPT classifies “hateful” and “offensive” as subsets of “toxic.” Moreover, the choice of prompts used to interact with ChatGPT impacts its performance. Based on these insights, our study provides several meaningful implications for employing ChatGPT to detect HOT content, particularly regarding the reliability and consistency of its performance, its understanding and reasoning of the HOT concept, and the impact of prompts on its performance. Overall, our study provides guidance on the potential of using generative AI models for moderating large volumes of user-generated textual content on social media.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139689758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 2","authors":"Hao Peng, Jian Yang, Jia Wu, Philip S. Yu","doi":"10.1145/3631941","DOIUrl":"https://doi.org/10.1145/3631941","url":null,"abstract":"<p>No abstract available.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139397965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BNoteHelper: A Note-Based Outline Generation Tool for Structured Learning on Video Sharing Platforms","authors":"Fangyu Yu, Peng Zhang, Xianghua Ding, Tun Lu, Ning Gu","doi":"10.1145/3638775","DOIUrl":"https://doi.org/10.1145/3638775","url":null,"abstract":"<p>Usually generated by ordinary users and often not particularly designed for learning, the videos on video sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video and aiming to consume the video itself. In this paper, we design and implement BNoteHelper, a note-based video outline prototype which generates outline titles by extracting user-generated notes on Bilibili, using the BART model fine-tuned on a built dataset. As a browser plugin, BNoteHelper provides users with video overview and navigation as well as note-taking template, via two main features: outline table and navigation marker. The model and prototype are evaluated through automatic and human evaluations. The automatic evaluation reveals that, both before and after fine-tuning, the BART model outperforms T5-Pegasus in BLEU and Perplexity metrics. Also, the results from user feedback reveal that the generation outline sourced from notes is preferred by users than that sourced from video captions due to its more concise, clear, and accurate characteristics, but also too general with less details and diversities sometimes. Two features of the video outline are also found to have respective advantages specially in holistic and fine-grained aspects. Based on these results, we propose insights into designing a video summary from the user-generated creation perspective, customizing it based on video types, and strengthening the advantages of its different visual styles on video sharing platforms.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139053162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. Manipulation","authors":"Alisa Rieger, Tim Draws, Mariët Theune, Nava Tintarev","doi":"10.1145/3635034","DOIUrl":"https://doi.org/10.1145/3635034","url":null,"abstract":"<p>When people use web search engines to find information on debated topics, the search results they encounter can influence opinion formation and practical decision-making with potentially far-reaching consequences for the individual and society. However, current web search engines lack support for information-seeking strategies that enable responsible opinion formation, e.g., by mitigating confirmation bias and motivating engagement with diverse viewpoints. We conducted two preregistered user studies to test the benefits and risks of an intervention aimed at confirmation bias mitigation. In the first study, we tested the effect of warning labels, warning of the risk of confirmation bias, combined with obfuscations, hiding selected search results per default. We observed that obfuscations with warning labels effectively reduce engagement with search results. These initial findings did not allow conclusions about the extent to which the reduced engagement was caused by the warning label (reflective nudging element) versus the obfuscation (automatic nudging element). If obfuscation was the primary cause, this would raise concerns about harming user autonomy. We thus conducted a follow-up study to test the effect of warning labels and obfuscations separately. </p><p>According to our findings, obfuscations run the risk of manipulating behavior instead of guiding it, while warning labels without obfuscations (purely reflective) do not exhaust processing capacities but encourage users to actively choose to decrease engagement with attitude-confirming search results. Therefore, given the risks and unclear benefits of obfuscations and potentially other automatic nudging elements to guide engagement with information, we call for prioritizing interventions that aim to enhance human cognitive skills and agency instead.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Performance of Twitter Users: A Predictor of Subjective Well-Being during the Pandemic","authors":"Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang","doi":"10.1145/3635033","DOIUrl":"https://doi.org/10.1145/3635033","url":null,"abstract":"<p>The outbreak of the COVID-19 pandemic triggered the perils of misinformation over social media. By amplifying the spreading speed and popularity of trustworthy information, influential social media users have been helping overcome the negative impacts of such flooding misinformation. In this paper, we use the COVID-19 pandemic as a representative global health crisis and and examine the impact of the COVID-19 pandemic on these influential users’ subjective well-being (SWB), one of the most important indicators of mental health. We leverage Twitter as a representative social media platform and conduct the analysis with our collection of 37,281,824 tweets spanning almost two years. To identify influential Twitter users, we propose a new measurement called <i>user bridging performance</i> (UBM) to evaluate the speed and wideness gain of information transmission due to their sharing. With our tweet collection, we manage to reveal the more significant mental sufferings of influential users during the COVID-19 pandemic. According to this observation, through comprehensive <i>hierarchical multiple regression analysis</i>, we are the first to discover the <i>strong relationship</i> between individual social users’ subjective well-being and their bridging performance. We proceed to extend <i>bridging performance</i> from individuals to user subgroups. The new measurement allows us to conduct a subgroup analysis according to users’ multilingualism and confirm the bridging role of multilingual users in the COVID-19 information propagation. We also find that multilingual users not only suffer from a much lower SWB in the pandemic, but also experienced a more significant SWB drop.</p>","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138495114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiresolution Local Spectral Attributed Community Search","authors":"Qingqing Li, Huifang Ma, Zhixin Li, Liang Chang","doi":"10.1145/3624580","DOIUrl":"https://doi.org/10.1145/3624580","url":null,"abstract":"Community search has become especially important in graph analysis task, which aims to identify latent members of a particular community from a few given nodes. Most of the existing efforts in community search focus on exploring the community structure with a single scale in which the given nodes are located. Despite promising results, the following two insights are often neglected. First, node attributes provide rich and highly related auxiliary information apart from network interactions for characterizing the node properties. Attributes may indicate the community assignment of a node with very few links, which would be difficult to determine from the network structure alone. Second, the multiresolution community affords latent information to depict the hierarchical relation of the network and ensure that one of them is closest to the real one. It is essential for users to understand the underlying structure of the network and explore the community with strong structure and attribute cohesiveness at disparate scales. These aspects motivate us to develop a new community search framework called Multiresolution Local Spectral Attributed Community Search (MLSACS). Specifically, inspired by the local modularity, graph wavelets, and scaling functions, we propose a new Multiresolution Local modularity (MLQ) based on a reconstructed node attribute graph. Furthermore, to detect local communities with cohesive structures and attributes at different scales, a sparse indicator vector is developed based on MLQ by solving a linear programming problem. Extensive experimental results on both synthetic and real-world attributed graphs have demonstrated the detected communities are meaningful and the scale can be changed reasonably.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triangle-oriented Community Detection considering Node Features and Network Topology","authors":"Guangliang Gao, Weichao Liang, Ming Yuan, Hanwei Qian, Qun Wang, Jie Cao","doi":"10.1145/3626190","DOIUrl":"https://doi.org/10.1145/3626190","url":null,"abstract":"The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Graph-Based Context-Aware Model to Understand Online Conversations","authors":"Vibhor Agarwal, Anthony P. Young, Sagar Joglekar, Nishanth Sastry","doi":"10.1145/3624579","DOIUrl":"https://doi.org/10.1145/3624579","url":null,"abstract":"Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. However, in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations’ surrounding contexts should improve the model’s performance for the inference task at hand. We propose GraphNLI , 1 a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples “nearby” comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment’s embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and find that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro- F 1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively. We also perform extensive ablative experiments and hyperparameter searches to understand the efficacy of GraphNLI. This demonstrates the potential of context-aware models to capture the global context along with the local context of online conversations for these two tasks.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UNDERSTANDING RUG PULLS: AN IN-DEPTH BEHAVIORAL ANALYSIS OF FRAUDULENT NFT CREATORS","authors":"Trishie Sharma, Rachit Agarwal, Sandeep Kumar Shukla","doi":"10.1145/3623376","DOIUrl":"https://doi.org/10.1145/3623376","url":null,"abstract":"The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles and an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, the fraudulent schemes that steal investors’ funds. From a curated dataset of 760 rug pulls across 10 NFT marketplaces, we examine these schemes’ structural and behavioral properties, identify the characteristics and motivations of rug pullers, and classify NFT projects into 20 groups based on creators’ association with their accounts. Our findings reveal that repeated rug pulls account for a significant proportion of the rise in NFT-related cryptocurrency crimes, with one NFT creator attempting 37 rug pulls within 3 months. Additionally, we identify the largest group of creators influencing the majority of rug pulls and demonstrate the connection between rug pullers of different NFT projects using the same wallets to store and move money. Our study contributes to understanding NFT market risks and provides insights for designing preventative strategies to mitigate future losses.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136057942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}