Online Social Networks and Media最新文献_第10页

EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets EMOCOV:利用COVID-19推文进行情绪检测、分析和可视化的机器学习

Online Social Networks and Media Pub Date : 2021-05-01 DOI: 10.1016/j.osnem.2021.100135

Md. Yasin Kabir, Sanjay Madria

{"title":"EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets","authors":"Md. Yasin Kabir, Sanjay Madria","doi":"10.1016/j.osnem.2021.100135","DOIUrl":"10.1016/j.osnem.2021.100135","url":null,"abstract":"<div><p>The adversarial impact of the Covid-19 pandemic has created a health crisis globally all over the world. This unprecedented crisis forced people to lockdown and changed almost every aspect of the regular activities of the people. Thus, the pandemic is also impacting everyone physically, mentally, and economically, and it, therefore, is paramount to analyze and understand emotional responses during the crisis affecting mental health. Negative emotional responses at fine-grained labels like anger and fear during the crisis might also lead to irreversible socio-economic damages. In this work, we develop a neural network model and train it using manually labeled data to detect various emotions at fine-grained labels in the Covid-19 tweets automatically. We present a manually labeled tweets dataset on COVID-19 emotional responses along with regular tweets data. We created a custom Q&A roBERTa model to extract phrases from the tweets that are primarily responsible for the corresponding emotions. None of the existing datasets and work currently provide the selected words or phrases denoting the reason for the corresponding emotions. Our classification model outperforms other systems and achieves a Jaccard score of 0.6475 with an accuracy of 0.8951. The custom RoBERTa Q&A model outperforms other models by achieving a Jaccard score of 0.7865. Further, we present a historical emotion analysis using COVID-19 tweets over the USA including each state level analysis.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10451311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets 一个社交媒体分析平台，通过利用自动标记地理位置的推文，可视化COVID-19在意大利的传播

Online Social Networks and Media Pub Date : 2021-05-01 DOI: 10.1016/j.osnem.2021.100134

Stelios Andreadis, Gerasimos Antzoulatos, Thanassis Mavropoulos, Panagiotis Giannakeris, Grigoris Tzionis, Nick Pantelidis, Konstantinos Ioannidis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris

{"title":"A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets","authors":"Stelios Andreadis, Gerasimos Antzoulatos, Thanassis Mavropoulos, Panagiotis Giannakeris, Grigoris Tzionis, Nick Pantelidis, Konstantinos Ioannidis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris","doi":"10.1016/j.osnem.2021.100134","DOIUrl":"10.1016/j.osnem.2021.100134","url":null,"abstract":"<div><p>Social media play an important role in the daily life of people around the globe and users have emerged as an active part of news distribution as well as production. The threatening pandemic of COVID-19 has been the lead subject in online discussions and posts, resulting to large amounts of related social media data, which can be utilised to reinforce the crisis management in several ways. Towards this direction, we propose a novel framework to collect, analyse, and visualise Twitter posts, which has been tailored to specifically monitor the virus spread in severely affected Italy. We present and evaluate a deep learning localisation technique that geotags posts based on the locations mentioned in their text, a face detection algorithm to estimate the number of people appearing in posted images, and a community detection approach to identify communities of Twitter users. Moreover, we propose further analysis of the collected posts to predict their reliability and to detect trending topics and events. Finally, we demonstrate an online platform that comprises an interactive map to display and filter analysed posts, utilising the outcome of the localisation technique, and a visual analytics dashboard that visualises the results of the topic, community, and event detection methodologies.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10804761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

A behavioural analysis of credulous Twitter users 对轻信的Twitter用户的行为分析

Online Social Networks and Media Pub Date : 2021-05-01 DOI: 10.1016/j.osnem.2021.100133

Alessandro Balestrucci , Rocco De Nicola , Marinella Petrocchi , Catia Trubiani

{"title":"A behavioural analysis of credulous Twitter users","authors":"Alessandro Balestrucci , Rocco De Nicola , Marinella Petrocchi , Catia Trubiani","doi":"10.1016/j.osnem.2021.100133","DOIUrl":"10.1016/j.osnem.2021.100133","url":null,"abstract":"<div><p><span>Thanks to platforms such as Twitter and Facebook, people can know facts and events that otherwise would have been silenced. However, social media significantly contribute also to fast spreading biased and false news while targeting specific segments of the population. We have seen how false information can be spread using automated accounts, known as bots. Using Twitter as a benchmark, we investigate behavioural attitudes of so called ‘credulous’ users, i.e., genuine accounts following many bots. Leveraging our previous work, where supervised learning is successfully applied to single out credulous users, we improve the classification task with a detailed features’ analysis and provide evidence that simple and lightweight features are crucial to detect such users. Furthermore, we study the differences in the way credulous and not credulous users interact with bots and discover that credulous users tend to amplify more the content posted by bots and argue that their detection can be instrumental to get useful information on possible dissemination of </span>spam content, propaganda, and, in general, little or no reliable information.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122210660","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

Special Issue on Disinformation, Hoaxes and Propaganda within Online Social Networks and Media 网上社交网络和媒体中的虚假信息、骗局和宣传特刊

Online Social Networks and Media Pub Date : 2021-05-01 DOI: 10.1016/j.osnem.2021.100132

Yelena Mejova , Marinella Petrocchi , Carolina Scarton

引用次数: 3

Debate on online social networks at the time of COVID-19: An Italian case study COVID-19时期关于在线社交网络的争论:以意大利为例

Online Social Networks and Media Pub Date : 2021-05-01 DOI: 10.1016/j.osnem.2021.100136

Martino Trevisan, Luca Vassio, Danilo Giordano

{"title":"Debate on online social networks at the time of COVID-19: An Italian case study","authors":"Martino Trevisan, Luca Vassio, Danilo Giordano","doi":"10.1016/j.osnem.2021.100136","DOIUrl":"10.1016/j.osnem.2021.100136","url":null,"abstract":"<div><p>The COVID-19 pandemic is not only having a heavy impact on healthcare but also changing people’s habits and the society we live in. Countries such as Italy have enforced a total lockdown lasting several months, with most of the population forced to remain at home. During this time, online social networks, more than ever, have represented an alternative solution for social life, allowing users to interact and debate with each other. Hence, it is of paramount importance to understand the changing use of social networks brought about by the pandemic. In this paper, we analyze how the interaction patterns around popular influencers in Italy changed during the first six months of 2020, within Instagram and Facebook social networks. We collected a large dataset for this group of public figures, including more than 54 million comments on over 140 thousand posts for these months. We analyze and compare engagement on the posts of these influencers and provide quantitative figures for aggregated user activity. We further show the changes in the patterns of usage before and during the lockdown, which demonstrated a growth of activity and sizable daily and weekly variations. We also analyze the user sentiment through the psycholinguistic properties of comments, and the results testified the rapid boom and disappearance of topics related to the pandemic. To support further analyses, we release the anonymized dataset.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10804760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

CoVerifi: A COVID-19 news verification system CoVerifi:新冠肺炎新闻核查系统

Online Social Networks and Media Pub Date : 2021-03-01 DOI: 10.1016/j.osnem.2021.100123

Nikhil L. Kolluri , Dhiraj Murthy

{"title":"CoVerifi: A COVID-19 news verification system","authors":"Nikhil L. Kolluri , Dhiraj Murthy","doi":"10.1016/j.osnem.2021.100123","DOIUrl":"10.1016/j.osnem.2021.100123","url":null,"abstract":"<div><p>There is an abundance of misinformation, disinformation, and “fake news” related to COVID-19, leading the director-general of the World Health Organization to term this an ‘infodemic’. Given the high volume of COVID-19 content on the Internet, many find it difficult to evaluate veracity. Vulnerable and marginalized groups are being misinformed and subject to high levels of stress. Riots and panic buying have also taken place due to “fake news”. However, individual research-led websites can make a major difference in terms of providing accurate information. For example, the Johns Hopkins Coronavirus Resource Center website has over 81 million entries linked to it on Google. With the outbreak of COVID-19 and the knowledge that deceptive news has the potential to measurably affect the beliefs of the public, new strategies are needed to prevent the spread of misinformation. This study seeks to make a timely intervention to the information landscape through a COVID-19 “fake news”, misinformation, and disinformation website. In this article, we introduce CoVerifi, a web application which combines both the power of machine learning and the power of human feedback to assess the credibility of news. By allowing users the ability to “vote” on news content, the CoVerifi platform will allow us to release labelled data as open source, which will enable further research on preventing the spread of COVID-19-related misinformation. We discuss the development of CoVerifi and the potential utility of deploying the system at scale for combating the COVID-19 “infodemic”.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10391703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45

Information disorders during the COVID-19 infodemic: The case of Italian Facebook COVID-19信息大流行期间的信息紊乱:以意大利Facebook为例

Online Social Networks and Media Pub Date : 2021-03-01 DOI: 10.1016/j.osnem.2021.100124

Stefano Guarino , Francesco Pierri , Marco Di Giovanni , Alessandro Celestini

{"title":"Information disorders during the COVID-19 infodemic: The case of Italian Facebook","authors":"Stefano Guarino , Francesco Pierri , Marco Di Giovanni , Alessandro Celestini","doi":"10.1016/j.osnem.2021.100124","DOIUrl":"10.1016/j.osnem.2021.100124","url":null,"abstract":"<div><p>The recent COVID-19 pandemic came alongside with an “infodemic”, with online social media flooded by often unreliable information associating the medical emergency with popular subjects of disinformation. In Italy, one of the first European countries suffering a rise in new cases and dealing with a total lockdown, controversial topics such as migrant flows and the 5G technology were often associated online with the origin and diffusion of the virus. In this work we analyze COVID-19 related conversations on the Italian Facebook, collecting over 1.5M posts shared by nearly 80k public pages and groups for a period of four months since January 2020. On the one hand, our findings suggest that well-known unreliable sources had a limited exposure, and that discussions over controversial topics did not spark a comparable engagement with respect to institutional and scientific communication. On the other hand, however, we realize that dis- and counter-information induced a polarization of (clusters of) groups and pages, wherein conversations were characterized by a topical lexicon, by a great diffusion of user generated content, and by link-sharing patterns that seem ascribable to coordinated propaganda. As revealed by the URL-sharing diffusion network showing a “small-world” effect, users were easily exposed to harmful propaganda as well as to verified information on the virus, exalting the role of public figures and mainstream media, as well as of Facebook groups, in shaping the public opinion.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39483599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Covid notions: Towards formal definitions – and documented understanding – of privacy goals and claimed protection in proximity-tracing services Covid概念:对邻近跟踪服务中的隐私目标和声称的保护进行正式定义和文档化理解

Online Social Networks and Media Pub Date : 2021-03-01 DOI: 10.1016/j.osnem.2021.100125

Christiane Kuhn, Martin Beck, Thorsten Strufe

{"title":"Covid notions: Towards formal definitions – and documented understanding – of privacy goals and claimed protection in proximity-tracing services","authors":"Christiane Kuhn, Martin Beck, Thorsten Strufe","doi":"10.1016/j.osnem.2021.100125","DOIUrl":"10.1016/j.osnem.2021.100125","url":null,"abstract":"<div><p>The recent SARS-CoV-2 pandemic gave rise to management approaches using mobile apps for contact tracing. The corresponding apps track individuals and their interactions, to facilitate alerting users of potential infections well before they become infectious themselves. Naïve implementation obviously jeopardizes the privacy of health conditions, location, activities, and social interaction of its users. A number of protocol designs for colocation tracking have already been developed, most of which claim to function in a privacy preserving manner. However, despite claims such as “GDPR compliance”, “anonymity”, “pseudonymity” or other forms of “privacy”, the authors of these designs usually neglect to precisely define what they (aim to) protect.</p><p>We make a first step towards formally defining the privacy notions of proximity tracing services, especially with regards to the health, (co-)location, and social interaction of their users. We also give a high-level intuition of which protection the most prominent proposals likely can and cannot achieve. This initial overview indicates that all proposals include some centralized services, and none protects identity and (co-)locations of infected users perfectly from both other users and the service provider.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2021.100125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25446407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

FactRank: Developing automated claim detection for Dutch-language fact-checkers FactRank:为荷兰语事实核查员开发自动索赔检测

Online Social Networks and Media Pub Date : 2021-03-01 DOI: 10.1016/j.osnem.2020.100113

Bettina Berendt , Peter Burger , Rafael Hautekiet , Jan Jagers , Alexander Pleijter , Peter Van Aelst

{"title":"FactRank: Developing automated claim detection for Dutch-language fact-checkers","authors":"Bettina Berendt , Peter Burger , Rafael Hautekiet , Jan Jagers , Alexander Pleijter , Peter Van Aelst","doi":"10.1016/j.osnem.2020.100113","DOIUrl":"10.1016/j.osnem.2020.100113","url":null,"abstract":"<div><p>Fact-checking has always been a central task of journalism, but given the ever-growing amount and speed of news offline and online, as well as the growing amounts of misinformation and disinformation, it is becoming increasingly important to support human fact-checkers with (semi-)automated methods to make their work more efficient. Within fact-checking, the detection of check-worthy claims is a crucial initial step, since it limits the number of claims that require or deserve to be checked for their truthfulness.</p><p>In this paper, we present FactRank, a novel claim detection tool for journalists specifically created for the Dutch language. To the best of our knowledge, this is the first and still the only such tool for Dutch. FactRank thus complements existing online claim detection tools for English and (a small number of) other languages. FactRank performs similarly to claim detection in ClaimBuster, the state-of-the-art fact-checking tool for English. Our comparisons with a human baseline also indicate that given how much even expert human fact-checkers disagree, there may be a natural “upper bound” on the accuracy of check-worthiness detection by machine-learning methods.</p><p>The specific quality of FactRank derives from the interdisciplinary and iterative process in which it was created, which includes not only a high-performance deep-learning neural network architecture, but also a principled approach to defining and operationalising the concept of check-worthiness via a detailed codebook. This codebook was created jointly by expert fact-checkers from the two countries that have Dutch as an official language (Belgium/Flanders and the Netherlands). We expect FactRank to be very useful exactly because of the way we defined check-worthiness, and because of how we have made this explicit and traceable.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2020.100113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121216451","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: 11

An exploratory study of COVID-19 misinformation on Twitter 推特上关于COVID-19错误信息的探索性研究

Online Social Networks and Media Pub Date : 2021-03-01 DOI: 10.1016/j.osnem.2020.100104

Gautam Kishore Shahi , Anne Dirkson , Tim A. Majchrzak

{"title":"An exploratory study of COVID-19 misinformation on Twitter","authors":"Gautam Kishore Shahi , Anne Dirkson , Tim A. Majchrzak","doi":"10.1016/j.osnem.2020.100104","DOIUrl":"10.1016/j.osnem.2020.100104","url":null,"abstract":"<div><p>During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.osnem.2020.100104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10388065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 248