Proceedings of the 32nd ACM Conference on Hypertext and Social Media最新文献_第3页

Incorporating the Measurement of Moral Foundations Theory into Analyzing Stances on Controversial Topics 把道德基础理论的测量纳入争议性问题的立场分析

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-25 DOI: 10.1145/3465336.3475112

R. Rezapour, Ly Dinh, J. Diesner

{"title":"Incorporating the Measurement of Moral Foundations Theory into Analyzing Stances on Controversial Topics","authors":"R. Rezapour, Ly Dinh, J. Diesner","doi":"10.1145/3465336.3475112","DOIUrl":"https://doi.org/10.1145/3465336.3475112","url":null,"abstract":"This paper investigates the correlation between moral foundations and the expression of opinions in the form of stance on different issues of public interest. This work is based on the assumption that the formation of values (personal and societal) and language are interrelated, and that we can observe differences in points of view in user-generated text data. We leverage the Moral Foundations Theory to expand the scope of stance analysis by examining the narratives in favor or against several topics. Applying an expanded version of the Moral Foundations Dictionary to a benchmark dataset for stance analysis, we capture and analyze the relationships between moral values and polarized online discussions. Using this enhanced methodology, we find that each social issue has different \"moral and lexical profiles.\" While some social issues project more authority related words (Donald Trump), others consists of words related to care and purity (abortion and feminism). Our correlation analysis of stance and morality revealed notable associations between stances on social issues and various types of morality, such as care, fairness, and loyalty, hence demonstrating that there are certain morality types that are more attributed to stance classification than others. Overall, our analysis highlights the usefulness of considering morality when studying stance. The differences observed in various viewpoints and stances highlights linguistic variation in discourse, which may assist in analyzing cultural values and biases in society.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133263787","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: 13

Death and Transmediations: Manuscripts in the Age of Hypertext 死亡与调解:超文本时代的手稿

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-25 DOI: 10.1145/3465336.3475093

A. Antonini, Francesca Benatti, Nicola Watson, Edmund G. C. King, Jonathan Gibson

{"title":"Death and Transmediations: Manuscripts in the Age of Hypertext","authors":"A. Antonini, Francesca Benatti, Nicola Watson, Edmund G. C. King, Jonathan Gibson","doi":"10.1145/3465336.3475093","DOIUrl":"https://doi.org/10.1145/3465336.3475093","url":null,"abstract":"Has Hypertext killed off both the form and value of manuscript? Digital authoring first and web authoring later have changed drastically the availability and type of traces that reflect both creative and editorial processes. In this view, the consolidated approaches on manuscript studies involving the analysis of material artefacts are challenged. While new methodologies such as digital \"forensics\" and \"virtual desks\" are emerging, the nature and relations of native-digital manuscripts are yet to be fully investigated. This contribution accounts digital artefacts within the field of manuscript studies, identifying parallels between material manuscripts and hypertext features in their value as documents. The mapping between digital and material artefacts outlines a theory of manuscript \"transmediations\" identifying where and how manuscripts cues are reflected in digital technologies. This theory is developed through case studies and analyses of digital transitions. In a discussion, we highlight key challenges and future directions for scholarly editions of digital manuscripts. Lastly, we elaborate the requirements of a hypertext \"genre\" for digital manuscripts that supports reconciling the open-ended collaborative process of curation with the need for a coherent narrative addressed to the broader public.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125086598","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

Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors 通过心理和动机因素分析社交媒体上的假新闻传播者

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-24 DOI: 10.1145/3465336.3475097

Mansooreh Karami, Tahora H. Nazer, Huan Liu

引用次数: 19

Weakly Supervised Cross-platform Teenager Detection with Adversarial BERT 基于对抗BERT的弱监督跨平台青少年检测

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-24 DOI: 10.1145/3465336.3475105

Peiling Yi, A. Zubiaga

引用次数: 0

Exploring the Links between Personality Traits and Susceptibility to Disinformation 探索人格特质与对虚假信息的易感性之间的联系

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-11 DOI: 10.1145/3465336.3475121

Dipto Barman, Owen Conlan

引用次数: 1

Cross-lingual Capsule Network for Hate Speech Detection in Social Media 社交媒体中仇恨言论检测的跨语言胶囊网络

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-06 DOI: 10.1145/3465336.3475102

Aiqi Jiang, A. Zubiaga

引用次数: 5

You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights 你也是，布鲁图斯!在社交媒体中诱捕可恶的用户:挑战，解决方案和见解

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-01 DOI: 10.1145/3465336.3475106

Mithun Das, Punyajoy Saha, Ritam Dutt, Pawan Goyal, Animesh Mukherjee, Binny Mathew

{"title":"You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights","authors":"Mithun Das, Punyajoy Saha, Ritam Dutt, Pawan Goyal, Animesh Mukherjee, Binny Mathew","doi":"10.1145/3465336.3475106","DOIUrl":"https://doi.org/10.1145/3465336.3475106","url":null,"abstract":"Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to find hateful posts and subsequently identify hateful users. However, this methodology disregards the social connections between users. In this paper, we run a detailed exploration of the problem space and investigate an array of models ranging from purely textual to graph based to finally semi-supervised techniques using Graph Neural Networks (GNN) that utilize both textual and graph-based features. We run exhaustive experiments on two datasets -- Gab, which is loosely moderated and Twitter, which is strictly moderated. Overall the AGNN model achieves 0.791 macro F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset using only 5% of the labeled instances, considerably outperforming all the other models including the fully supervised ones. We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures and the AGNN model benefits by paying attention to these signatures. This property, as we observe, also allows the model to generalize well across platforms in a zero-shot setting. Lastly, we utilize the best performing GNN model to analyze the evolution of hateful users and their targets over time in Gab.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115697416","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: 16

Towards Enhancing Blind Users' Interaction Experience with Online Videos via Motion Gestures 通过动作手势增强盲人用户与网络视频的交互体验

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-08-01 DOI: 10.1145/3465336.3475116

H. Lee, V. Ashok

{"title":"Towards Enhancing Blind Users' Interaction Experience with Online Videos via Motion Gestures","authors":"H. Lee, V. Ashok","doi":"10.1145/3465336.3475116","DOIUrl":"https://doi.org/10.1145/3465336.3475116","url":null,"abstract":"Blind users interact with smartphone applications using a screen reader, an assistive technology that enables them to navigate and listen to application content using touch gestures. Since blind users rely on screen reader audio, interacting with online videos can be challenging due to the screen reader audio interfering with the video sounds. Existing solutions to address this interference problem are predominantly designed for desktop scenarios, where special keyboard or mouse actions are supported to facilitate 'silent' and direct access to various video controls such as play, pause, and progress bar. As these solutions are not transferable to smartphones, suitable alternatives are desired. In this regard, we explore the potential of motion gestures in smartphones as an effective and convenient method for blind screen reader users to interact with online videos. Specifically, we designed and developed YouTilt, an Android application that enables screen reader users to exploit an assortment of motion gestures to access and manipulate various video controls. We then conducted a user study with 10 blind participants to investigate whether blind users can leverage YouTilt to properly execute motion gestures for video-interaction tasks while simultaneously listening to video sounds. Analysis of the study data showed a significant improvement in usability by as much as 43.3% (avg.) with YouTilt compared to that with default screen reader, and overall a positive attitude and acceptance towards motion gesture-based video interaction.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117283853","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

DECIFE: Detecting Collusive Users Involved in Blackmarket Following Services on Twitter DECIFE:在Twitter上发现参与黑市跟踪服务的串通用户

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-07-24 DOI: 10.1145/3465336.3475108

Hridoy Sankar Dutta, Kartik Aggarwal, Tanmoy Chakraborty

{"title":"DECIFE: Detecting Collusive Users Involved in Blackmarket Following Services on Twitter","authors":"Hridoy Sankar Dutta, Kartik Aggarwal, Tanmoy Chakraborty","doi":"10.1145/3465336.3475108","DOIUrl":"https://doi.org/10.1145/3465336.3475108","url":null,"abstract":"The popularity of Twitter has fostered the emergence of various fraudulent user activities - one such activity is to artificially bolster the social reputation of Twitter profiles by gaining a large number of followers within a short time span. Many users want to gain followers to increase the visibility and reach of their profiles to wide audiences. This has provoked several blackmarket services to garner huge attention by providing artificial followers via the network of agreeable and compromised accounts in a collusive manner. Their activity is difficult to detect as the blackmarket services shape their behavior in such a way that users who are part of these services disguise themselves as genuine users. In this paper, we propose DECIFE, a framework to detect collusive users involved in producing 'following' activities through blackmarket services with the intention to gain collusive followers in return. We first construct a heterogeneous user-tweet-topic network to leverage the follower/followee relationships and linguistic properties of a user. The heterogeneous network is then decomposed to form four different subgraphs that capture the semantic relations between the users. An attention-based subgraph aggregation network is proposed to learn and combine the node representations from each subgraph. The combined representation is finally passed on to a hypersphere learning objective to detect collusive users. Comprehensive experiments on our curated dataset are conducted to validate the effectiveness of DECIFE by comparing it with other state-of-the-art approaches. To our knowledge, this is the first attempt to detect collusive users involved in blackmarket 'following services' on Twitter.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130889688","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

Structack: Structure-based Adversarial Attacks on Graph Neural Networks 结构:图神经网络上基于结构的对抗性攻击

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-07-23 DOI: 10.1145/3465336.3475110

Hussain Hussain, Tomislav Duricic, E. Lex, D. Helic, M. Strohmaier, Roman Kern

{"title":"Structack: Structure-based Adversarial Attacks on Graph Neural Networks","authors":"Hussain Hussain, Tomislav Duricic, E. Lex, D. Helic, M. Strohmaier, Roman Kern","doi":"10.1145/3465336.3475110","DOIUrl":"https://doi.org/10.1145/3465336.3475110","url":null,"abstract":"Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approaches are typically informed, i.e. they have access to information about node attributes such as labels and feature vectors. In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes. Here the attacker aims to exploit structural knowledge and assumptions, which GNN models make about graph data. In particular, literature has shown that structural node centrality and similarity have a strong influence on learning with GNNs. Therefore, we study the impact of centrality and similarity on adversarial attacks on GNNs. We demonstrate that attackers can exploit this information to decrease the performance of GNNs by focusing on injecting links between nodes of low similarity and, surprisingly, low centrality. We show that structure-based uninformed attacks can approach the performance of informed attacks, while being computationally more efficient. With our paper, we present a new attack strategy on GNNs that we refer to as Structack. Structack can successfully manipulate the performance of GNNs with very limited information while operating under tight computational constraints. Our work contributes towards building more robust machine learning approaches on graphs.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132301463","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: 5