Proceedings of the 32nd ACM Conference on Hypertext and Social Media最新文献_第4页

Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages 消除多语言词嵌入的偏见:以三种印度语言为例

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-07-21 DOI: 10.1145/3465336.3475118

Srijan Bansal, Vishal Garimella, Ayush Suhane, Animesh Mukherjee

引用次数: 6

"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak “病毒没有宗教信仰”:分析COVID-19爆发期间推特上的伊斯兰恐惧症

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-07-11 DOI: 10.1145/3465336.3475111

Mohit Chandra, Manvith Reddy, Shradha Sehgal, Saurabh Gupta, Arun Balaji Buduru, P. Kumaraguru

{"title":"\"A Virus Has No Religion\": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak","authors":"Mohit Chandra, Manvith Reddy, Shradha Sehgal, Saurabh Gupta, Arun Balaji Buduru, P. Kumaraguru","doi":"10.1145/3465336.3475111","DOIUrl":"https://doi.org/10.1145/3465336.3475111","url":null,"abstract":"The COVID-19 pandemic has disrupted people's lives driving them to act in fear, anxiety, and anger, leading to worldwide racist events in the physical world and online social networks. Though there are works focusing on Sinophobia during the COVID-19 pandemic, less attention has been given to the recent surge in Islamophobia. A large number of positive cases arising out of the religious Tablighi Jamaat gathering has driven people towards forming anti-Muslim communities around hashtags like #coronajihad, #tablighijamaatvirus on Twitter. In addition to the online spaces, the rise in Islamophobia has also resulted in increased hate crimes in the real world. Hence, an investigation is required to create interventions. To the best of our knowledge, we present the first large-scale quantitative study linking Islamophobia with COVID-19. In this paper, we present CoronaBias dataset which focuses on anti-Muslim hate spanning four months, with over 410,990 tweets from 244,229 unique users. We use this dataset to perform longitudinal analysis. We find the relation between the trend on Twitter with the offline events that happened over time, measure the qualitative changes in the context associated with the Muslim community, and perform macro and micro topic analysis to find prevalent topics. We also explore the nature of the content, focusing on the toxicity of the URLs shared within the tweets present in the CoronaBias dataset. Apart from the content-based analysis, we focus on user analysis, revealing that the portrayal of religion as a symbol of patriotism played a crucial role in deciding how the Muslim community was perceived during the pandemic. Through these experiments, we reveal the existence of anti-Muslim rhetoric around COVID-19 in the Indian sub-continent.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721939","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: 24

Are Word Embedding Methods Stable and Should We Care About It? 词嵌入方法稳定吗?我们应该关注它吗?

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2021-04-17 DOI: 10.1145/3465336.3475098

Angana Borah, M. Barman, Amit Awekar

引用次数: 8

Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries 通往白宫之路:美国总统初选中主流媒体与社交媒体的关系分析

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2020-09-19 DOI: 10.1145/3465336.3475115

Aaron Brookhouse, Tyler Derr, Hamid Karimi, H. Bernard, Jiliang Tang

{"title":"Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries","authors":"Aaron Brookhouse, Tyler Derr, Hamid Karimi, H. Bernard, Jiliang Tang","doi":"10.1145/3465336.3475115","DOIUrl":"https://doi.org/10.1145/3465336.3475115","url":null,"abstract":"Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities primarily utilized newspapers and later radio and television to inform the public, the political arena has transformed into a more complex structure with the rise of the Internet and online social media. Now, more than ever, people express themselves online while mainstream news agencies attempt to utilize the power of the Internet to spread their articles as much as possible. To grasp the political coexistence of mainstream media and online social media, in this paper, we analyze these two sources of information in the context of the U.S. 2020 presidential election. In particular, we collected data during the 2020 Democratic Party presidential primaries pertaining to the candidates, and, by analyzing this data, we highlight similarities and differences between these two main types of sources, detect the potential impact they have on each other, and understand how this impact relationship can change over time.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126901324","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

CrisisBERT: A Robust Transformer for Crisis Classification and Contextual Crisis Embedding CrisisBERT:危机分类和上下文危机嵌入的鲁棒转换器

Proceedings of the 32nd ACM Conference on Hypertext and Social Media Pub Date : 2020-05-11 DOI: 10.1145/3465336.3475117

Junhua Liu, Trisha Singhal, L. Blessing, Kristin L. Wood, Kwan Hui Lim

{"title":"CrisisBERT: A Robust Transformer for Crisis Classification and Contextual Crisis Embedding","authors":"Junhua Liu, Trisha Singhal, L. Blessing, Kristin L. Wood, Kwan Hui Lim","doi":"10.1145/3465336.3475117","DOIUrl":"https://doi.org/10.1145/3465336.3475117","url":null,"abstract":"Detecting crisis events accurately is an important task, as it allows the relevant authorities to implement necessary actions to mitigate damages. For this purpose, social media serve as a timely information source due to its prevalence and high volume of first-hand accounts. While there are prior works on crises detection, many of them do not perform crisis embedding and classification using state-of-the-art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. In contrast, we propose CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and F1 scores. The proposed CrisisBERT model demonstrates superior robustness over various benchmarks, and it includes only marginal performance compromise while extending from 6 to 36 events with a mere 51.4% additional data points. We also propose Crisis2Vec, an attention-based, document-level contextual embedding architecture, for crisis embedding, which achieves better performance than conventional crisis embedding methods such as Word2Vec and GloVe.","PeriodicalId":325072,"journal":{"name":"Proceedings of the 32nd ACM Conference on Hypertext and Social Media","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114483414","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: 42