International Conference on Web and Social Media最新文献_第4页

Representing and Determining Argumentative Relevance in Online Discussions: A General Approach 在线讨论中论证相关性的表征和确定:一般方法

International Conference on Web and Social Media Pub Date : 2023-06-02 DOI: 10.1609/icwsm.v17i1.22146

Zhen Guo, Munindar P. Singh

{"title":"Representing and Determining Argumentative Relevance in Online Discussions: A General Approach","authors":"Zhen Guo, Munindar P. Singh","doi":"10.1609/icwsm.v17i1.22146","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22146","url":null,"abstract":"Understanding an online argumentative discussion is essential for understanding users' opinions on a topic and their underlying reasoning. A key challenge in determining completeness and persuasiveness of argumentative discussions is to assess how arguments under a topic are connected in a logical and coherent manner. Online argumentative discussions, in contrast to essays or face-to-face communication, challenge techniques for judging argument relevance because online discussions involve multiple participants and often exhibit incoherence in reasoning and inconsistencies in writing style. \u0000\u0000We define relevance as the logical and topical connections between small texts representing argument fragments in online discussions. We provide a corpus comprising pairs of sentences, labeled with argumentative relevance between the sentences in each pair. We propose a computational approach relying on content reduction and a Siamese neural network architecture for modeling argumentative connections and determining argumentative relevance between texts. \u0000\u0000Experimental results indicate that our approach is effective in measuring relevance between arguments, and outperforms strong and well-adopted baselines.\u0000Further analysis demonstrates the benefit of using our argumentative relevance encoding on a downstream task, predicting how impactful an online comment is to certain topic, comparing to encoding that does not consider logical connection.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288178","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

HateMM: A Multi-Modal Dataset for Hate Video Classification 仇恨视频分类的多模态数据集

International Conference on Web and Social Media Pub Date : 2023-05-06 DOI: 10.48550/arXiv.2305.03915

Mithun Das, R. Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, Animesh Mukherjee

{"title":"HateMM: A Multi-Modal Dataset for Hate Video Classification","authors":"Mithun Das, R. Raj, Punyajoy Saha, Binny Mathew, Manish Gupta, Animesh Mukherjee","doi":"10.48550/arXiv.2305.03915","DOIUrl":"https://doi.org/10.48550/arXiv.2305.03915","url":null,"abstract":"Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage automated video moderation techniques are needed to handle the videos that are being uploaded to keep the platform safe and healthy. With a view to detect and remove hateful content from the video sharing platforms, our work focuses on hate video detection using multi-modalities. To this end, we curate ~43 hours of videos from BitChute and manually annotate them as hate or non-hate, along with the frame spans which could explain the labelling decision. To collect the relevant videos we harnessed search keywords from hate lexicons. We observe various cues in images and audio of hateful videos. Further, we build deep learning multi-modal models to classify the hate videos and observe that using all the modalities of the videos improves the overall hate speech detection performance (accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modal model in terms of macro F1 score. In summary, our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131867648","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

Reddit in the Time of COVID 新冠疫情时期的Reddit

International Conference on Web and Social Media Pub Date : 2023-04-21 DOI: 10.48550/arXiv.2304.10777

V. Veselovsky, Ashton Anderson

{"title":"Reddit in the Time of COVID","authors":"V. Veselovsky, Ashton Anderson","doi":"10.48550/arXiv.2304.10777","DOIUrl":"https://doi.org/10.48550/arXiv.2304.10777","url":null,"abstract":"When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online platforms suddenly fulfilled. However, researchers lack a rigorous understanding of the pandemic's impacts on social platforms---and whether they were temporary or long-lasting. We present a conceptual framework for studying the large-scale evolution of social platforms and apply it to the study of Reddit's history, with a particular focus on the COVID-19 pandemic. We study platform evolution through two key dimensions: structure vs. content and macro- vs. micro-level analysis. Structural signals help us quantify how much behavior changed, while content analysis clarifies exactly how it changed. Applying these at the macro-level illuminates platform-wide changes, while at the micro-level we study impacts on individual users. We illustrate the value of this approach by showing the extraordinary and ordinary changes Reddit went through during the pandemic. First, we show that typically when rapid growth occurs, it is driven by a few concentrated communities and within a narrow slice of language use. However, Reddit's growth throughout COVID-19 was spread across disparate communities and languages. Second, all groups were equally affected in their change of interest, but veteran users tended to invoke COVID-related language more than newer users. Third, the new wave of users that arrived following COVID-19 was fundamentally different from previous cohorts of new users in terms of interests, activity, and likelihood of staying active on the platform. These findings provide a more rigorous understanding of how an online platform changed during the global pandemic.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127223732","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

Auditing Elon Musk's Impact on Hate Speech and Bots 审计埃隆·马斯克对仇恨言论和机器人的影响

International Conference on Web and Social Media Pub Date : 2023-04-09 DOI: 10.48550/arXiv.2304.04129

Daniel Hickey, Matheus Schmitz, D. Fessler, P. Smaldino, Goran Muric, K. Burghardt

引用次数: 11

Truth Social Dataset 真实社会数据集

International Conference on Web and Social Media Pub Date : 2023-03-20 DOI: 10.48550/arXiv.2303.11240

Patrick Gérard, Nicholas Botzer, Tim Weninger

{"title":"Truth Social Dataset","authors":"Patrick Gérard, Nicholas Botzer, Tim Weninger","doi":"10.48550/arXiv.2303.11240","DOIUrl":"https://doi.org/10.48550/arXiv.2303.11240","url":null,"abstract":"Formally announced to the public following former President Donald Trump’s bans and suspensions from mainstream social networks in early 2022 following his role in the January 6 Capitol Riots, Truth Social was launched as an ``alternative'' social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content moderation policies of then existing, mainstream social networks. The subsequent rise of Truth Social has been driven largely by hard-line supporters of the former president as well as those affected by the content moderation of other social networks. These distinct qualities combined with the its status as the main mouthpiece of the former president positions Truth Social as a particularly influential social media platform and give rise to several research questions. However, outside of a handful of news reports, little is known about the new social media platform partially due to a lack of well-curated data. In the current work, we describe a dataset of over 823,000 posts to Truth Social and and social network with over 454,000 distinct users. In addition to the dataset itself, we also present some basic analysis of its content, certain temporal features, and its network.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114651601","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

Wiki-based Communities of Interest: Demographics and Outliers 基于维基的兴趣社区:人口统计和异常值

International Conference on Web and Social Media Pub Date : 2023-03-16 DOI: 10.48550/arXiv.2303.09189

Hiba Arnaout, S. Razniewski, Jeff Z. Pan

引用次数: 0

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election 后续集群、卫星受众和桥梁节点:2020年美国大选的协同参与网络

International Conference on Web and Social Media Pub Date : 2023-02-28 DOI: 10.48550/arXiv.2303.04620

Andrew Beers, Joseph S. Schafer, Ian Kennedy, Morgan Wack, Emma S. Spiro, Kate Starbird

{"title":"Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election","authors":"Andrew Beers, Joseph S. Schafer, Ian Kennedy, Morgan Wack, Emma S. Spiro, Kate Starbird","doi":"10.48550/arXiv.2303.04620","DOIUrl":"https://doi.org/10.48550/arXiv.2303.04620","url":null,"abstract":"The 2020 United States (US) presidential election was — and has continued to be — the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027434","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: 3

#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy #罗伊被推翻:关于堕胎权争议的推特数据集

International Conference on Web and Social Media Pub Date : 2023-02-02 DOI: 10.48550/arXiv.2302.01439

Rong-Ching Chang, Ashwin Rao, Qiankun Zhong, Magdalena E. Wojcieszak, Kristina Lerman

引用次数: 8

Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic 冲击下的团队弹性:COVID-19大流行早期GitHub存储库的实证分析

International Conference on Web and Social Media Pub Date : 2023-01-29 DOI: 10.48550/arXiv.2301.12326

Xuan Lu, W. Ai, Yixin Wang, Q. Mei

{"title":"Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic","authors":"Xuan Lu, W. Ai, Yixin Wang, Q. Mei","doi":"10.48550/arXiv.2301.12326","DOIUrl":"https://doi.org/10.48550/arXiv.2301.12326","url":null,"abstract":"While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown. Software developers have relied on remote collaborations long before the pandemic, working in virtual teams (GitHub repositories). The dynamics of these repositories through the pandemic provide a unique opportunity to understand how remote teams react under shock. This work presents a systematic analysis. \u0000\u0000We measure the overall effect of the early pandemic on public GitHub repositories by comparing their sizes and productivity with the counterfactual outcomes forecasted as if there were no pandemic. We find that the productivity level and the number of active members of these teams vary significantly during different periods of the pandemic. We then conduct a finer-grained investigation and study the heterogeneous effects of the shock on individual teams. We find that the resilience of a team is highly correlated to certain properties of the team before the pandemic. Through a bootstrapped regression analysis, we reveal which types of teams are robust or fragile to the shock.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115461790","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

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community 作者作为角色和叙述者:从r/AmITheAsshole Reddit社区解构个人叙事

International Conference on Web and Social Media Pub Date : 2023-01-19 DOI: 10.48550/arXiv.2301.08104

Salvatore Giorgi, Ke Zhao, Alexander H. Feng, Lara J. Martin

{"title":"Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community","authors":"Salvatore Giorgi, Ke Zhao, Alexander H. Feng, Lara J. Martin","doi":"10.48550/arXiv.2301.08104","DOIUrl":"https://doi.org/10.48550/arXiv.2301.08104","url":null,"abstract":"In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is \"the asshole\"). These first person narratives are, in general, a unique storytelling domain where the author is not only the narrator (the person telling the story) but is also a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and (2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that \"assholes\" as Characters frame themselves as lacking agency with a more positive personal arc, while \"assholes\" as Narrators will tell emotional and opinionated stories.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125969583","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