International Conference on Web and Social Media最新文献_第10页

The Contribution of Verified Accounts to Self-Disclosure in COVID-Related Twitter Conversations 在与新冠病毒相关的推特对话中，认证账户对自我披露的贡献

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19394

Tingting Du, Prasanna Umar, S. Rajtmajer, A. Squicciarini

引用次数: 0

Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events 会话弹性:量化和预测不良事件后的会话结果

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19314

Charlotte L. Lambert, A. Rajagopal, Eshwar Chandrasekharan

{"title":"Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events","authors":"Charlotte L. Lambert, A. Rajagopal, Eshwar Chandrasekharan","doi":"10.1609/icwsm.v16i1.19314","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19314","url":null,"abstract":"Online conversations, just like offline ones, are susceptible to influence by bad actors. These users have the capacity to derail neutral or even prosocial discussions through adverse behavior. Moderators and users alike would benefit from more resilient online conversations, i.e., those that can survive the influx of adverse behavior to which many conversations fall victim. In this paper, we examine the notion of conversational resilience: what makes a conversation more or less capable of withstanding an adverse interruption? Working with 11.5M comments from eight mainstream subreddits, we compiled more than 5.8M comment threads (i.e., conversations). Using 239K relevant conversations, we examine how well comment, user, and subreddit characteristics can predict conversational outcomes. More than half of all conversations proceed after the first adverse event. Six out of ten conversations that proceed result in future removals. Comments violating platform-wide norms and those written by authors with a history of norm violations lead to not only more norm violations, but also fewer prosocial outcomes. However, conversations in more populated subreddits and conversations where the first adverse event's author was initially a strong contributor are capable of minimizing future removals and promoting prosocial outcomes after an adverse event. By understanding factors that contribute to conversational resilience we shed light onto what types of behavior can be encouraged to promote prosocial outcomes even in the face of adversity.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115838805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset FaCov: COVID-19病毒新闻和谣言事实核查文章数据集

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19383

Shakshi Sharma, Ekanshi Agrawal, Rajesh Sharma, Anwitaman Datta

{"title":"FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset","authors":"Shakshi Sharma, Ekanshi Agrawal, Rajesh Sharma, Anwitaman Datta","doi":"10.1609/icwsm.v16i1.19383","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19383","url":null,"abstract":"COVID-19, which was first detected in late 2019 in Wuhan, China, has spread to the rest of the world and is currently deemed a global pandemic. A flux of events triggered by a wide ranging set of factors such as virus mutations and waves of infections, imperfect medical and policy interventions, and vested interest driven political posturing all have created a continuous state of uncertainty and strife. In this verbile environment, misinformation and fake news thrive and propagate easily through the modern efficient all-pervading media and social media tools, resulting in an infodemic running its course in conjunction with the pandemic. In this work, we present a COVID-19 related dataset – FaCov – a compilation of fact-checking articles that examine and evaluate some of the most widely circulated rumors and claims concerning the coronavirus. We have collected articles from 13 very popular fact-checking sources, along with information about the articles and the vetted verity assigned to the claims being evaluated. We also share insights into the dataset to highlight and understand the major conversations and conflicts in narratives encompassing the pandemic.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"32 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116378388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

FairLP: Towards Fair Link Prediction on Social Network Graphs FairLP:面向社交网络图的公平链接预测

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19321

Yanying Li, Xiuling Wang, Yue Ning, Hui Wang

{"title":"FairLP: Towards Fair Link Prediction on Social Network Graphs","authors":"Yanying Li, Xiuling Wang, Yue Ning, Hui Wang","doi":"10.1609/icwsm.v16i1.19321","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19321","url":null,"abstract":"Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil the existence of bias in six existing state-of-the-art link prediction algorithms through extensive empirical studies over real world datasets. Third, we identify the imbalanced density across intra-group and inter-group links in training graphs as one of the underlying causes of bias in link prediction. Based on the identified cause, fourth, we design a pre-processing bias mitigation method named FairLP to modify the training graph, aiming to balance the distribution of intra-group and inter-group links while preserving the network characteristics of the graph. FairLP is model-agnostic and thus is compatible with any existing link prediction algorithm. Our experimental results on real-world social network graphs demonstrate that FairLP achieves better trade-off between fairness and prediction accuracy than the existing fairness-enhancing link prediction methods.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123840876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook 通过机器学习教育培养自我宣传技能:Facebook广告推荐案例

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19337

Yim Register, Emma S. Spiro

{"title":"Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook","authors":"Yim Register, Emma S. Spiro","doi":"10.1609/icwsm.v16i1.19337","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19337","url":null,"abstract":"Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or \"rabbit hole\" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine learning in real-world context -- an instructive web application using real examples built from the user's own FB data on ad interests. The instruction also prompts users to reflect on their interactions with ML systems, specifically Facebook. In a between-subjects design, we tested both Data Science Novices and Experts on the efficacy of the UB-CF instruction. Taking care to highlight the voices of marginalized users, we use the application as a prompt for surfacing potential harms perpetuated by FB ad recommendations, and qualitatively analyze themes of harm and proposed solutions provided by users themselves. The instruction increased comprehension of UB-CF for both groups, and we show that comprehension is associated with mentioning the mechanisms of the algorithm more in advocacy statements, a crucial component of a successful argument. We provide recommendations for increased algorithmic transparency on social media and for including marginalized voices in the conversation of algorithmic harm that are of interest both to social media researchers and ML educators.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131024540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks 社交网络上对抗图像净化鲁棒语义表示的自适应聚类

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19350

S. Silva, Arun Das, A. Alaeddini, Peyman Najafirad

{"title":"Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks","authors":"S. Silva, Arun Das, A. Alaeddini, Peyman Najafirad","doi":"10.1609/icwsm.v16i1.19350","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19350","url":null,"abstract":"Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes.\u0000One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. \u0000Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114860954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Zero-Shot Cross-Lingual Hate Speech Detection with Pseudo-Label Fine-Tuning of Transformer Language Models 基于变形语言模型的伪标签微调改进零采样跨语言仇恨语音检测

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19402

Haris Bin Zia, Ignacio Castro, A. Zubiaga, Gareth Tyson

引用次数: 11

Exploring the Magnitude and Effects of Media Influence on Reddit Moderation 探索媒体对Reddit适度影响的幅度和效果

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19291

Hussam Habib, Rishab Nithyanand

{"title":"Exploring the Magnitude and Effects of Media Influence on Reddit Moderation","authors":"Hussam Habib, Rishab Nithyanand","doi":"10.1609/icwsm.v16i1.19291","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19291","url":null,"abstract":"Most platforms, including Reddit, face a dilemma when applying interventions such as subreddit bans to toxic communities — do they risk angering their user base by proactively enforcing stricter controls on discourse or do they defer interventions at the risk of eventually triggering negative media reactions which might impact their advertising revenue? In this paper, we analyze Reddit’s previous administrative interventions to understand one aspect of this dilemma: the relationship between the media and administrative interventions. More specifically, we make two primary contributions. First, using a mediation analysis framework, we find evidence that Reddit’s interventions for violating their content policy for toxic content occur because of media pressure. Second, using interrupted time series analysis, we show that media attention on communities with toxic content only increases the problematic behavior associated with that community (both within the community itself and across the platform). However, we find no significant difference in the impact of administrative interventions on subreddits with and without media pressure. Taken all together, this study provides evidence of a media-driven moderation strategy at Reddit and also suggests that such a strategy may not have a significantly different impact than a more proactive strategy.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116215525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Using Authorship Verification to Mitigate Abuse in Online Communities 使用作者身份验证来减少在线社区中的滥用

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19359

Janith Weerasinghe, Rhia Singh, R. Greenstadt

{"title":"Using Authorship Verification to Mitigate Abuse in Online Communities","authors":"Janith Weerasinghe, Rhia Singh, R. Greenstadt","doi":"10.1609/icwsm.v16i1.19359","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19359","url":null,"abstract":"Social media has become an important method for information sharing. This has also created opportunities for bad actors to easily spread disinformation and manipulate public opinion. This paper explores the possibility of applying Authorship Verification on online communities to mitigate abuse by analyzing the writing style of online accounts to identify accounts managed by the same person. We expand on our similarity-based authorship verification approach, previously applied on large fanfictions, and show that it works in open-world settings, shorter documents, and is largely topic-agnostic. Our expanded model can link Reddit accounts based on the writing style of only 40 comments with an AUC of 0.95, and the performance increases to 0.98 given more content. We apply this model on a set of suspicious Reddit accounts associated with the disinformation campaign surrounding the 2016 U.S. presidential election and show that the writing style of these accounts are inconsistent, indicating that each account was likely maintained by multiple individuals. We also apply this model to Reddit user accounts that commented on the WallStreetBets subreddit around the 2021 GameStop short squeeze and show that a number of account pairs share very similar writing styles. We also show that this approach can link accounts across Reddit and Twitter with an AUC of 0.91 even when training data is very limited.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125572617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter 病毒式帖子对专业人士可见度和行为的影响:对Twitter上科学家的纵向研究

International Conference on Web and Social Media Pub Date : 2022-05-31 DOI: 10.1609/icwsm.v16i1.19295

Rakibul Hasan, C. Cheyre, Yong-Yeol Ahn, Roberto Hoyle, Apu Kapadia

{"title":"The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter","authors":"Rakibul Hasan, C. Cheyre, Yong-Yeol Ahn, Roberto Hoyle, Apu Kapadia","doi":"10.1609/icwsm.v16i1.19295","DOIUrl":"https://doi.org/10.1609/icwsm.v16i1.19295","url":null,"abstract":"On social media, due to complex interactions between users' attention and recommendation algorithms, the visibility of users' posts can be unpredictable and vary wildly, sometimes creating unexpected viral events for `ordinary’ users. How do such events affect users' subsequent behaviors and long-term visibility on the platform? We investigate these questions following a matching-based framework using a dataset comprised of tweeting activities and follower graph changes of 17,157 scientists on Twitter. We identified scientists who experienced `unusual' virality for the first time in their profile lifespan (`viral' group) and quantified how viral events influence tweeting behaviors and popularity (as measured through follower statistics). After virality, the viral group increased tweeting frequency, their tweets became more objective and focused on fewer topics, and expressed more positive sentiment relative to their pre-virality tweets. Also, their post-virality tweets were more aligned with their professional expertise and similar to the viral tweet compared to past tweets. Finally, the viral group gained more followers in both the short and long terms compared to a control group.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115155958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4