Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining最新文献_第10页

Multi-stage graph peeling algorithm for probabilistic core decomposition 概率核分解的多阶段图剥离算法

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-08-13 DOI: 10.1145/3487351.3489470

Yang Guo, Xuekui Zhang, F. Esfahani, Venkatesh Srinivasan, Alex Thomo, Li Xing

{"title":"Multi-stage graph peeling algorithm for probabilistic core decomposition","authors":"Yang Guo, Xuekui Zhang, F. Esfahani, Venkatesh Srinivasan, Alex Thomo, Li Xing","doi":"10.1145/3487351.3489470","DOIUrl":"https://doi.org/10.1145/3487351.3489470","url":null,"abstract":"Mining dense subgraphs where vertices connect closely with each other is a common task when analyzing graphs. A very popular notion in subgraph analysis is core decomposition. Recently, Esfahani et al. presented a probabilistic core decomposition algorithm based on graph peeling and Central Limit Theorem (CLT) that is capable of handling very large graphs. Their proposed peeling algorithm (PA) starts from the lowest degree vertices and recursively deletes these vertices, assigning core numbers, and updating the degree of neighbour vertices until it reached the maximum core. However, in many applications, particularly in biology, more valuable information can be obtained from dense sub-communities and we are not interested in small cores where vertices do not interact much with others. To make the previous PA focus more on dense subgraphs, we propose a multi-stage graph peeling algorithm (M-PA) that has a two-stage data screening procedure added before the previous PA. After removing vertices from the graph based on the user-defined thresholds, we can reduce the graph complexity largely and without affecting the vertices in subgraphs that we are interested in. We show that M-PA is more efficient than the previous PA and with the properly set filtering threshold, can produce very similar if not identical dense subgraphs to the previous PA (in terms of graph density and clustering coefficient).","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681757","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

Group-node attention for community evolution prediction 群体节点关注的群落进化预测

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-07-09 DOI: 10.1145/3487351.3488348

Matt Revelle, C. Domeniconi, Ben U. Gelman

引用次数: 1

BotRGCN: Twitter bot detection with relational graph convolutional networks BotRGCN: Twitter机器人检测与关系图卷积网络

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-06-24 DOI: 10.1145/3487351.3488336

Shangbin Feng, Herun Wan, Ningnan Wang, Minnan Luo

引用次数: 42

Full Bitcoin blockchain data made easy 完整的比特币区块链数据变得很容易

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-06-15 DOI: 10.1145/3487351.3488326

Jules Azad Emery, Matthieu Latapy

引用次数: 4

What's kooking?: characterizing India's emerging social network, Koo 疯子是什么?印度新兴社交网络的特点

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-03-24 DOI: 10.1145/3487351.3488354

A. Singh, Chirag Jain, Jivitesh Jain, R. Jain, Shradha Sehgal, Tanisha Pandey, P. Kumaraguru

{"title":"What's kooking?: characterizing India's emerging social network, Koo","authors":"A. Singh, Chirag Jain, Jivitesh Jain, R. Jain, Shradha Sehgal, Tanisha Pandey, P. Kumaraguru","doi":"10.1145/3487351.3488354","DOIUrl":"https://doi.org/10.1145/3487351.3488354","url":null,"abstract":"Social media has grown exponentially in a short period, coming to the forefront of communications and online interactions. Despite their rapid growth, social media platforms have been unable to scale to different languages globally and remain inaccessible to many. In this paper, we characterize Koo, a multilingual micro-blogging site that rose in popularity in 2021, as an Indian alternative to Twitter. We collected a dataset of 4.07 million users, 163.12 million follower-following relationships, and their content and activity across 12 languages. We study the user demographic along the lines of language, location, gender, and profession. The prominent presence of Indian languages in the discourse on Koo indicates the platform's success in promoting regional languages. We observe Koo's follower-following network to be much denser than Twitter's, comprising of closely-knit linguistic communities. An N-gram analysis of posts on Koo shows a #KooVsTwitter rhetoric, revealing the debate comparing the two platforms. Our characterization highlights the dynamics of the multilingual social network and its diverse Indian user base.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772270","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

Examining factors associated with Twitter account suspension following the 2020 U.S. presidential election 正在研究2020年美国总统大选后推特账户被关闭的相关因素

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-01-23 DOI: 10.1145/3487351.3492715

Farhan Asif Chowdhury, Dheeman Saha, Md Rashidul Hasan, Koustuv Saha, A. Mueen

{"title":"Examining factors associated with Twitter account suspension following the 2020 U.S. presidential election","authors":"Farhan Asif Chowdhury, Dheeman Saha, Md Rashidul Hasan, Koustuv Saha, A. Mueen","doi":"10.1145/3487351.3492715","DOIUrl":"https://doi.org/10.1145/3487351.3492715","url":null,"abstract":"Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safeguard against actors violating their rules. However, the correlation between publicly outlined policies and employed action is less clear to general people. In this work, we examine violations and subsequent moderations related to the 2020 U.S. President Election discussion on Twitter. We focus on quantifying plausible reasons for the suspension, drawing on Twitter's rules and policies by identifying suspended users (Case) and comparing their activities and properties with (yet) non-suspended (Control) users. Using a dataset of 240M election-related tweets made by 21M unique users, we observe that Suspended users violate Twitter's rules at a higher rate (statistically significant) than Control users across all the considered aspects - hate speech, offensiveness, spamming, and civic integrity. Moreover, through the lens of Twitter's suspension mechanism, we qualitatively examine the targeted topics for manipulation.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312570","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

From #jobsearch to #mask: improving COVID-19 cascade prediction with spillover effects 从#jobsearch到#mask:改进具有溢出效应的COVID-19级联预测

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2020-12-13 DOI: 10.1145/3487351.3488555

Ninghan Chen, Zhiqiang Zhong, Jun Pang

{"title":"From #jobsearch to #mask: improving COVID-19 cascade prediction with spillover effects","authors":"Ninghan Chen, Zhiqiang Zhong, Jun Pang","doi":"10.1145/3487351.3488555","DOIUrl":"https://doi.org/10.1145/3487351.3488555","url":null,"abstract":"An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129661949","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

Racism is a virus: anti-asian hate and counterspeech in social media during the COVID-19 crisis 种族主义是一种病毒:在2019冠状病毒病危机期间，社交媒体上出现了反亚洲的仇恨和反言论

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2020-05-25 DOI: 10.1145/3487351.3488324

Caleb Ziems, Bing He, Sandeep Soni, Srijan Kumar

{"title":"Racism is a virus: anti-asian hate and counterspeech in social media during the COVID-19 crisis","authors":"Caleb Ziems, Bing He, Sandeep Soni, Srijan Kumar","doi":"10.1145/3487351.3488324","DOIUrl":"https://doi.org/10.1145/3487351.3488324","url":null,"abstract":"The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterspeech in mitigating this spread. In this work, we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months, containing over 206 million tweets, and a social network with over 127 million nodes. By creating a novel hand-labeled dataset of 3,355 tweets, we train a text classifier to identify hateful and counterspeech tweets that achieves an average macro-F1 score of 0.832. Using this dataset, we conduct longitudinal analysis of tweets and users. Analysis of the social network reveals that hateful and counterspeech users interact and engage extensively with one another, instead of living in isolated polarized communities. We find that nodes were highly likely to become hateful after being exposed to hateful content in the year 2020. Notably, counterspeech messages discourage users from turning hateful, potentially suggesting a solution to curb hate on web and social media platforms. Data and code is available at http://claws.cc.gatech.edu/covid.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123510855","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: 135

Fairness and diversity in the recommendation and ranking of participatory media content 参与性媒体内容推荐和排名的公平性和多样性

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2019-07-16 DOI: 10.1145/3487351.3488363

Muskaan, Mehak Preet Dhaliwal, Aaditeshwar Seth

引用次数: 9