Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining最新文献_第6页

Analyzing online opinions and influence campaigns on blogs using BlogTracker 使用BlogTracker分析在线意见和影响博客活动

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489483

Abiola Akinnubi, Nitin Agarwal, Zachary Kimo Stine, Sodiq Oyedotun

{"title":"Analyzing online opinions and influence campaigns on blogs using BlogTracker","authors":"Abiola Akinnubi, Nitin Agarwal, Zachary Kimo Stine, Sodiq Oyedotun","doi":"10.1145/3487351.3489483","DOIUrl":"https://doi.org/10.1145/3487351.3489483","url":null,"abstract":"Blogging has become an essential part of the new print media of the 21st century despite the emergence of social media platforms like Twitter and Facebook, with many news agencies, media outlets, journalists and users using this medium to write without any restriction on topics of choice or events that happen over the world. Although social networking sites have also become a hotbed where users share their views, it suffers from distraction when users try to air their views on topics that affect them due to character limitation, real-time toxic behavior, and content ownership rights. Social networking sites like Facebook, Twitter and Reddit are sometimes used to drive traffic to blogs sites. The blogosphere, defined as the network of blogs, is growing at an exponential rate. Medium.com and WordPress.com are among the top blogging platforms, with WordPress leading the way as a top blogging platform and followed by other platforms like Medium, Hashnode, Tumblr, and blogger. Analyzing blog data helps understand the pulse of a society, know what resonates with a community, and recognize the grievances of a group, among other reasons. Since there is no character limit in blogs, unlike Twitter, blogs allow much depth in discourse, allowing it to be an effective platform for setting narratives. Blogs also provide a convenient platform to develop situational awareness during a socio-political crisis or humanitarian crisis in a conflict-torn region or a disaster-struck area. To address the difficulty of having a publicly accessible blog data analytical solution since solutions like Blogdex, among others, were either discontinued or made proprietary, we present BlogTracker. This tool helps users analyze public discussions with real-time data update capability and analyze narratives and emotion distribution on associated blog posts and trackers. This demonstration shows how the BlogTracker application analyses blog data with a case study about COVID-19.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126453710","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

An auction-based mechanism to promote cooperation in resource exchange networks 建立以拍卖为基础的资源交换网络合作机制

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489476

Haripriya Chakraborty, Liang Zhao

引用次数: 1

Which kind of rumors may undermine society: perspectives from court orders 什么样的谣言会破坏社会:从法院命令的角度看

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488358

Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

引用次数: 0

The BiasChecker: how biased are social media searches? 偏见检查器:社交媒体搜索有多偏颇?

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489482

Can Yang, B. Nunes, J. Santos, S. Siqueira, Xinyuan Xu

引用次数: 1

Hot topic detection and evaluation of multi-relation effects 多关系效应的热点话题检测与评价

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3490972

Nadir Emre Zirbilek, M. Erakin, Tansel Özyer, R. Alhajj

引用次数: 1

A model for optimizing article recommendation for reducing polarization 减少极化的文章推荐优化模型

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488349

Inzamam Rahaman, Patrick Hosein

引用次数: 2

SentiStance: quantifying the intertwined changes of sentiment and stance in response to an event in online forums 情感立场:量化对在线论坛事件的情绪和立场的相互交织的变化

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3490966

Jakapun Tachaiya, Arman Irani, K. Esterling, M. Faloutsos

{"title":"SentiStance: quantifying the intertwined changes of sentiment and stance in response to an event in online forums","authors":"Jakapun Tachaiya, Arman Irani, K. Esterling, M. Faloutsos","doi":"10.1145/3487351.3490966","DOIUrl":"https://doi.org/10.1145/3487351.3490966","url":null,"abstract":"How are the sentiment and stance of online users affected by real-world events? Previous studies have ignored the role of events in co-determining sentiment and stance and hence have failed to understand the relationship between these two important aspects of public opinion. In this paper, we develop SentiStance, a systematic framework to understand the intertwined change of sentiment and stance due to real-world events in online discussions. In our approach: (a) we customize state-of-the-art NLP techniques to overcome domain-specific constraints, and (b) we provide an efficient way to quantify the change of sentiment and stance in tandem. As a case study, we focus on the 2020 United States Election events and we analyze 7.5 million posts from 4chan, Reddit, and Parler over a span of three months from November 2020 to January 2021. We showcase our framework by describing the effect that the Jan 6 insurrection had on concepts \"Pence\" and \"Trump.\" Parler users turn significantly against Pence with (33.1% increase in Against stance and Negative sentiment), while Reddit users' opinion improves (with a drop of 7.1% in the same combination of sentiment and stance). By contrast, the effect of the same event on the concept \"Trump\" shows no statistically significant change. In addition, our results suggest that conditioning on significant events strengthens the correlation between sentiment and stance, which provides a new perspective on the debate around the correlation between sentiment and stance. Overall, we see our work as a fundamental building block towards a data-driven understanding of the interplay of preferences and emotions of online forum users towards a concept.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124930955","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

A novel knowledge-based multi-population framework for studying opinion dynamics of migrated individuals 基于知识的迁移个体意见动态研究新框架

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488556

Sarvnaz Sadeghi, Pooya Moradian Zadeh

引用次数: 0

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information 基于网络级移动信息的COVID-19深度扩散预测

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488334

Padmaksha Roy, Shailik Sarkar, Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu

{"title":"Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information","authors":"Padmaksha Roy, Shailik Sarkar, Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu","doi":"10.1145/3487351.3488334","DOIUrl":"https://doi.org/10.1145/3487351.3488334","url":null,"abstract":"Modeling the spatiotemporal nature of the spread of infectious diseases can provide useful intuition in understanding the time-varying aspect of the disease spread and the underlying complex spatial dependency observed in people's mobility patterns. Besides, the county level multiple related time series information can be leveraged to make a forecast on an individual time series. Adding to this challenge is the fact that real-time data often deviates from the unimodal Gaussian distribution assumption and may show some complex mixed patterns. Motivated by this, we develop a deep learning-based time-series model for probabilistic forecasting called Auto-regressive Mixed Density Dynamic Diffusion Network (ARM3Dnet), which considers both people's mobility and disease spread as a diffusion process on a dynamic directed graph. The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the realtime data while learning from multiple related time series. We show that our model, when trained with the best combination of dynamic covariate features and mixture components, can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129286228","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

Analyzing topic attention in online small groups 分析在线小组的话题关注

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488357

J. Caetano, Jussara M. Almeida, Marcos André Gonçalves, Wagner Meira, Jr, H. T. Marques-Neto, V. Almeida

{"title":"Analyzing topic attention in online small groups","authors":"J. Caetano, Jussara M. Almeida, Marcos André Gonçalves, Wagner Meira, Jr, H. T. Marques-Neto, V. Almeida","doi":"10.1145/3487351.3488357","DOIUrl":"https://doi.org/10.1145/3487351.3488357","url":null,"abstract":"Attention is a scarce resource disputed by algorithms and people on the Internet. This competition for attention is part of online spaces especially online small groups where there is a limited number of individuals interacting with each other using text and media content that is not controlled by algorithms or human curators. In these groups, as certain participants and piece of content can catch the collective attention, a question that naturally arises is: how to analyze topic attention in online small groups? In this paper, we propose a methodology aimed at answering this question. Our proposal consists of sets of analyses over topical (obtained from topic analysis) transition graphs for characterizing attention allocation, permanence and shifting as well as participant role characterization during discussions in online small groups. We experimented with our methodology using WhatsApp groups as a case study. Among other results, we identified and characterized abrupt and smooth topic transitions as well as patterns of participant activity related to certain topics.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131650168","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