Proceedings of the 31st ACM Conference on Hypertext and Social Media最新文献_第5页

How to Assess the Exhaustiveness of Longitudinal Web Archives: A Case Study of the German Academic Web 如何评估纵向网络档案的详尽性:以德国学术网络为例

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-07-13 DOI: 10.1145/3372923.3404836

Michael Paris, R. Jäschke

{"title":"How to Assess the Exhaustiveness of Longitudinal Web Archives: A Case Study of the German Academic Web","authors":"Michael Paris, R. Jäschke","doi":"10.1145/3372923.3404836","DOIUrl":"https://doi.org/10.1145/3372923.3404836","url":null,"abstract":"Longitudinal web archives can be a foundation for investigating structural and content-based research questions. One prerequisite is that they contain a faithful representation of the relevant subset of the web. Therefore, an assessment of the authority of a given dataset with respect to a research question should precede the actual investigation. Next to proper creation and curation, this requires measures for estimating the potential of a longitudinal web archive to yield information about the central objects the research question aims to investigate. In particular, content-based research questions often lack the ab-initio confidence about the integrity of the data. In this paper we focus on one specifically important aspect, namely the exhaustiveness of the dataset with respect to the central objects. Therefore, we investigate the recall coverage of researcher names in a longitudinal academic web crawl over a seven year period and the influence of our crawl method on the dataset integrity. Additionally, we propose a method to estimate the amount of missing information as a means to describe the exhaustiveness of the crawl and motivate a use case for the presented corpus.","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130854743","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

Augustine as "Naturalist of the Mind" 奥古斯丁是“心灵的自然主义者”

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-07-13 DOI: 10.1145/3372923.3404814

R. M. Simpson

引用次数: 1

You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism 你不能替我做决定!旅游业可解释群体聚集策略的评价

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-07-13 DOI: 10.1145/3372923.3404800

Shabnam Najafian, Daniel Herzog, S. Qiu, O. Inel, N. Tintarev

引用次数: 4

Wikipedia and Westminster: Quality and Dynamics of Wikipedia Pages about UK Politicians 维基百科和威斯敏斯特:关于英国政治家的维基百科页面的质量和动态

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-06-24 DOI: 10.1145/3372923.3404817

Pushkal Agarwal, Miriam Redi, Nishanth R. Sastry, E. Wood, Andrew Blick

{"title":"Wikipedia and Westminster: Quality and Dynamics of Wikipedia Pages about UK Politicians","authors":"Pushkal Agarwal, Miriam Redi, Nishanth R. Sastry, E. Wood, Andrew Blick","doi":"10.1145/3372923.3404817","DOIUrl":"https://doi.org/10.1145/3372923.3404817","url":null,"abstract":"Wikipedia is a major source of information providing a large variety of content online, trusted by readers from around the world. Readers go to Wikipedia to get reliable information about different subjects, one of the most popular being living people, and especially politicians. While a lot is known about the general usage and information consumption on Wikipedia, less is known about the life-cycle and quality of Wikipedia articles in the context of politics. The aim of this study is to quantify and qualify content production and consumption for articles about politicians, with a specific focus on UK Members of Parliament (MPs). First, we analyze spatio-temporal patterns of readers' and editors' engagement with MPs' Wikipedia pages, finding huge peaks of attention during election times, related to signs of engagement on other social media (e.g. Twitter). Second, we quantify editors' polarisation and find that most editors specialize in a specific party and choose specific news outlets as references. Finally we observe that the average citation quality is pretty high, with statements on 'Early life and career' missing citations most often (18%).","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127614666","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

Unsupervised Fake News Detection: A Graph-based Approach 无监督假新闻检测:基于图的方法

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-05-28 DOI: 10.1145/3372923.3404783

Siva Charan Reddy Gangireddy, D. P., Cheng Long, Tanmoy Chakraborty

{"title":"Unsupervised Fake News Detection: A Graph-based Approach","authors":"Siva Charan Reddy Gangireddy, D. P., Cheng Long, Tanmoy Chakraborty","doi":"10.1145/3372923.3404783","DOIUrl":"https://doi.org/10.1145/3372923.3404783","url":null,"abstract":"Fake news has become more prevalent than ever, correlating with the rise of social media that allows every user to rapidly publish their views or hearsay. Today, fake news spans almost every realm of human activity, across diverse fields such as politics and healthcare. Most existing methods for fake news detection leverage supervised learning methods and expect a large labelled corpus of articles and social media user engagement information, which are often hard, time-consuming and costly to procure. In this paper, we consider the task of unsupervised fake news detection, which considers fake news detection in the absence of labelled historical data. We develop GTUT, a graph-based approach for the task which operates in three phases. Starting off with identifying a seed set of fake and legitimate articles exploiting high-level observations on inter-user behavior in fake news propagation, it progressively expands the labelling to all articles in the dataset. Our technique draws upon graph-based methods such as biclique identification, graph-based feature vector learning and label spreading. Through an extensive empirical evaluation over multiple real-world datasets, we establish the improved effectiveness of our method over state-of-the-art techniques for the task.","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127558677","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: 47

Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model 社交媒体主题趋势叙事的概率模型:一个离散时间模型

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-04-14 DOI: 10.1145/3372923.3404790

Toktam A. Oghaz, Ece C. Mutlu, Jasser Jasser, Niloofar Yousefi, I. Garibay

{"title":"Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model","authors":"Toktam A. Oghaz, Ece C. Mutlu, Jasser Jasser, Niloofar Yousefi, I. Garibay","doi":"10.1145/3372923.3404790","DOIUrl":"https://doi.org/10.1145/3372923.3404790","url":null,"abstract":"Online social media platforms are turning into the prime source of news and narratives about worldwide events. However, a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data.","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131508001","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

What is BitChute?: Characterizing the 什么是BitChute?:描述

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-04-04 DOI: 10.1145/3372923.3404833

Milo Z. Trujillo, Mauricio G. Gruppi, C. Buntain, Benjamin D. Horne

{"title":"What is BitChute?: Characterizing the","authors":"Milo Z. Trujillo, Mauricio G. Gruppi, C. Buntain, Benjamin D. Horne","doi":"10.1145/3372923.3404833","DOIUrl":"https://doi.org/10.1145/3372923.3404833","url":null,"abstract":"In this paper, we characterize the content and discourse on BitChute, a social video-hosting platform. Launched in 2017 as an alternative to YouTube, BitChute joins an ecosystem of alternative, low content moderation platforms, including Gab, Voat, Minds, and 4chan. Uniquely, BitChute is the first of these alternative platforms to focus on video content and is growing in popularity. Our analysis reveals several key characteristics of the platform. We find that only a handful of channels receive any engagement, and almost all of those channels contain conspiracies or hate speech. This high rate of hate speech on the platform as a whole, much of which is anti-Semitic, is particularly concerning. Our results suggest that BitChute has a higher rate of hate speech than Gab but less than 4chan. Lastly, we find that while some BitChute content producers have been banned from other platforms, many maintain profiles on mainstream social media platforms, particularly YouTube. This paper contributes a first look at the content and discourse on BitChute and provides a building block for future research on low content moderation platforms.","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"114 1 Pt 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931581","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: 25

NesTPP: Modeling Thread Dynamics in Online Discussion Forums NesTPP:在线论坛中的线程动力学建模

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2020-03-12 DOI: 10.1145/3372923.3404796

Chen Ling, G. Tong, Mozi Chen

引用次数: 6

Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition 基于协同图划分的可扩展异构社会网络对齐

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2019-12-18 DOI: 10.1145/3372923.3404799

Yuxiang Ren, Lin Meng, Jiawei Zhang

{"title":"Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition","authors":"Yuxiang Ren, Lin Meng, Jiawei Zhang","doi":"10.1145/3372923.3404799","DOIUrl":"https://doi.org/10.1145/3372923.3404799","url":null,"abstract":"Social network alignment has been an important research problem for social network analysis in recent years. With the identified shared users across networks, it will provide researchers with the opportunity to achieve a more comprehensive understanding of users' social activities both within and across networks. Social network alignment is a very difficult problem. Besides the challenges introduced by the network heterogeneity, the network alignment can be reduced to a combinatorial optimization problem with an extremely large search space. The learning effectiveness and efficiency of existing alignment models will be degraded significantly as the network size increases. In this paper, we focus on studying the scalable heterogeneous social network alignment problem and propose to address it with a novel two-stage network alignment model, namely Scalable Heterogeneous Network Alignment (SHNA). Based on a group of intra- and inter-network meta diagrams, SHNA first partitions the social networks into a group of sub-networks synergistically. Via the partially known anchor links, SHNA can extract the partitioned sub-network correspondence relationships. Instead of aligning the complete input network, SHNA proposes to identify the anchor links between the matched sub-network pairs, while those between the unmatched sub-networks will be pruned to effectively shrink the search space. Extensive experiments have been done to compare SHNA with the state-of-the-art baseline methods on a real-world aligned social networks dataset. The experimental results have demonstrated both the effectiveness and efficiency of SHNA in addressing the problem.","PeriodicalId":389616,"journal":{"name":"Proceedings of the 31st ACM Conference on Hypertext and Social Media","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128199321","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

Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition 男人之于人，如同女人之于地点:命名实体识别中的性别偏见测量

Proceedings of the 31st ACM Conference on Hypertext and Social Media Pub Date : 2019-10-24 DOI: 10.1145/3372923.3404804

Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, A. Galstyan

引用次数: 47