International Conference on Web and Social Media最新文献_第5页

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter vaxx犹豫不决:一个研究推特上对COVID-19疫苗接种犹豫不决的数据集

International Conference on Web and Social Media Pub Date : 2023-01-17 DOI: 10.48550/arXiv.2301.06660

Yida Mu, Mali Jin, Charles Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song

{"title":"VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter","authors":"Yida Mu, Mali Jin, Charles Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song","doi":"10.48550/arXiv.2301.06660","DOIUrl":"https://doi.org/10.48550/arXiv.2301.06660","url":null,"abstract":"Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134407418","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: 6

A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns 协调加密货币相关的社交媒体活动的数据集

International Conference on Web and Social Media Pub Date : 2023-01-16 DOI: 10.1609/icwsm.v17i1.22219

Karolis Zilius, Tasos Spiliotopoulos, A. Moorsel

引用次数: 1

Measuring Belief Dynamics on Twitter 在Twitter上测量信念动态

International Conference on Web and Social Media Pub Date : 2022-11-22 DOI: 10.48550/arXiv.2211.11947

J. Introne

{"title":"Measuring Belief Dynamics on Twitter","authors":"J. Introne","doi":"10.48550/arXiv.2211.11947","DOIUrl":"https://doi.org/10.48550/arXiv.2211.11947","url":null,"abstract":"There is growing concern about misinformation and the role online media plays in social polarization. Analyzing belief dynamics is one way to enhance our understanding of these problems. Existing analytical tools, such as sur-vey research or stance detection, lack the power to corre-late contextual factors with population-level changes in belief dynamics. In this exploratory study, I present the Belief Landscape Framework, which uses data about people’s professed beliefs in an online setting to measure belief dynamics with more temporal granularity than previous methods. I apply the approach to conversations about climate change on Twitter and provide initial validation by comparing the method’s output to a set of hypotheses drawn from the literature on dynamic systems. My analysis indicates that the method is relatively robust to different parameter settings, and results suggest that 1) there are many stable configurations of belief on the polarizing issue of climate change and 2) that people move in predictable ways around these points. The method paves the way for more powerful tools that can be used to understand how the modern digital media eco-system impacts collective belief dynamics and what role misinformation plays in that process.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514205","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

SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media SexWEs:基于跨语言语义专门化的领域感知词嵌入在社交媒体中的中文性别歧视检测

International Conference on Web and Social Media Pub Date : 2022-11-15 DOI: 10.48550/arXiv.2211.08447

Aiqi Jiang, A. Zubiaga

{"title":"SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media","authors":"Aiqi Jiang, A. Zubiaga","doi":"10.48550/arXiv.2211.08447","DOIUrl":"https://doi.org/10.48550/arXiv.2211.08447","url":null,"abstract":"The goal of sexism detection is to mitigate negative online content targeting certain gender groups of people. However, the limited availability of labeled sexism-related datasets makes it problematic to identify online sexism for low-resource languages.\u0000In this paper, we address the task of automatic sexism detection in social media for one low-resource language -- Chinese. Rather than collecting new sexism data or building cross-lingual transfer learning models, we develop a cross-lingual domain-aware semantic specialisation system in order to make the most of existing data. Semantic specialisation is a technique for retrofitting pre-trained distributional word vectors by integrating external linguistic knowledge (such as lexico-semantic relations) into the specialised feature space. To do this, we leverage semantic resources for sexism from a high-resource language (English) to specialise pre-trained word vectors in the target language (Chinese) to inject domain knowledge. We demonstrate the benefit of our sexist word embeddings (SexWEs) specialised by our framework via intrinsic evaluation of word similarity and extrinsic evaluation of sexism detection. Compared with other specialisation approaches and Chinese baseline word vectors, our SexWEs shows an average score improvement of 0.033 and 0.064 in both intrinsic and extrinsic evaluations, respectively. The ablative results and visualisation of SexWEs also prove the effectiveness of our framework on retrofitting word vectors in low-resource languages.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780332","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

Weakly Supervised Learning for Analyzing Political Campaigns on Facebook 弱监督学习分析Facebook上的政治运动

International Conference on Web and Social Media Pub Date : 2022-10-19 DOI: 10.1609/icwsm.v17i1.22156

Tunazzina Islam, Shamik Roy, Dan Goldwasser

引用次数: 2

Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives - an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19 当代在线反犹太主义和阴谋叙事的代码、模式和形态——2019冠状病毒病背景下的注释指南和标记德语数据集

International Conference on Web and Social Media Pub Date : 2022-10-13 DOI: 10.48550/arXiv.2210.07934

Elisabeth Steffen, Helena Mihaljevic, Milena Pustet, Nyco Bischoff, María do Mar Castro Varela, Yener Bayramoğlu, Bahar Oghalai

{"title":"Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives - an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19","authors":"Elisabeth Steffen, Helena Mihaljevic, Milena Pustet, Nyco Bischoff, María do Mar Castro Varela, Yener Bayramoğlu, Bahar Oghalai","doi":"10.48550/arXiv.2210.07934","DOIUrl":"https://doi.org/10.48550/arXiv.2210.07934","url":null,"abstract":"Over the course of the COVID-19 pandemic, existing conspiracy theories were refreshed and new ones were created, often interwoven with antisemitic narratives, stereotypes and codes. The sheer volume of antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential for anti-discrimination organizations and researchers alike. However, the manifestation and dissemination of these two interrelated phenomena is still quite under-researched in scholarly empirical research of large text corpora. Algorithmic approaches for the detection and classification of specific contents usually require labeled datasets, annotated based on conceptually sound guidelines. While there is a growing number of datasets for the more general phenomenon of hate speech, the development of corpora and annotation guidelines for antisemitic and conspiracy content is still in its infancy, especially for languages other than English. \u0000To address this gap, we have developed an annotation guide for antisemitic and conspiracy theory online content in the context of the COVID-19 pandemic that includes working definitions, e.g. of specific forms of antisemitism such as encoded and post-Holocaust antisemitism. We use the guide to annotate a German-language dataset consisting of $sim ! 3,700$ Telegram messages sent between 03/2020 and 12/2021.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132544288","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

"A Special Operation": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems' Coverage of the Russo-Ukrainian War “一次特殊行动”:分析和比较不同媒体生态系统对俄乌战争报道的定量方法

International Conference on Web and Social Media Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.03016

Hans W. A. Hanley, Deepak Kumar, Z. Durumeric

{"title":"\"A Special Operation\": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems' Coverage of the Russo-Ukrainian War","authors":"Hans W. A. Hanley, Deepak Kumar, Z. Durumeric","doi":"10.48550/arXiv.2210.03016","DOIUrl":"https://doi.org/10.48550/arXiv.2210.03016","url":null,"abstract":"The coverage of the Russian invasion of Ukraine has varied widely between Western, Russian, and Chinese media ecosystems with propaganda, disinformation, and narrative spins present in all three. By utilizing the normalized pointwise mutual information metric, differential sentiment analysis, word2vec models, and partially labeled Dirichlet allocation, we present a quantitative analysis of the differences in coverage amongst these three news ecosystems. We find that while the Western press outlets have focused on the military and humanitarian aspects of the war, Russian media have focused on the purported justifications for the “special military operation” such as the presence in Ukraine of “bio-weapons” and “neo-nazis”, and Chinese news media have concentrated on the conflict’s diplomatic and economic consequences. Detecting the presence of several Russian disinformation narratives in the articles of several Chinese media outlets, we finally measure the degree to which Russian media has influenced Chinese coverage across Chinese outlets’ news articles, Weibo accounts, and Twitter accounts. Our analysis indicates that since the Russian invasion of Ukraine, Chinese state media outlets have increasingly cited Russian outlets as news sources and spread Russian disinformation narratives.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863903","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: 10

Differentially Private Propensity Scores for Bias Correction 偏差校正的差异私人倾向得分

International Conference on Web and Social Media Pub Date : 2022-10-05 DOI: 10.1609/icwsm.v17i1.22131

Liang Chen, Valentin Hartmann, Robert West

{"title":"Differentially Private Propensity Scores for Bias Correction","authors":"Liang Chen, Valentin Hartmann, Robert West","doi":"10.1609/icwsm.v17i1.22131","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22131","url":null,"abstract":"In surveys, it is typically up to the individuals to decide if they want to participate or not, which leads to participation bias: the individuals willing to share their data might not be representative of the entire population. Similarly, there are cases where one does not have direct access to any data of the target population and has to resort to publicly available proxy data sampled from a different distribution. In this paper, we present Differentially Private Propensity Scores for Bias Correction (DiPPS), a method for approximating the true data distribution of interest in both of the above settings. We assume that the data analyst has access to a dataset D' that was sampled from the distribution of interest in a biased way. As individuals may be more willing to share their data when given a privacy guarantee, we further assume that the analyst is allowed locally differentially private access to a set of samples D from the true, unbiased distribution. Each data point from the private, unbiased dataset D is mapped to a probability distribution over clusters (learned from the biased dataset D'), from which a single cluster is sampled via the exponential mechanism and shared with the data analyst. This way, the analyst gathers a distribution over clusters, which they use to compute propensity scores for the points in the biased D', which are in turn used to reweight the points in D' to approximate the true data distribution. It is now possible to compute any function on the resulting reweighted dataset without further access to the private D. In experiments on datasets from various domains, we show that DiPPS successfully brings the distribution of the available dataset closer to the distribution of interest in terms of Wasserstein distance. We further show that this results in improved estimates for different statistics, in many cases even outperforming differential privacy mechanisms that are specifically designed for these statistics.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787430","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

Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter 在推特上识别和描述QAnon阴谋中激进化的行为类别

International Conference on Web and Social Media Pub Date : 2022-09-19 DOI: 10.48550/arXiv.2209.09339

Emily L. Wang, Luca Luceri, Francesco Pierri, Emilio Ferrara

{"title":"Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter","authors":"Emily L. Wang, Luca Luceri, Francesco Pierri, Emilio Ferrara","doi":"10.48550/arXiv.2209.09339","DOIUrl":"https://doi.org/10.48550/arXiv.2209.09339","url":null,"abstract":"Social media provide a fertile ground where conspiracy theories and radical ideas can flourish, reach broad audiences, and sometimes lead to hate or violence beyond the online world itself. \u0000QAnon represents a notable example of a political conspiracy that started out on social media but turned mainstream, in part due to public endorsement by influential political figures. Nowadays, QAnon conspiracies often appear in the news, are part of political rhetoric, and are espoused by significant swaths of people in the United States. It is therefore crucial to understand how such a conspiracy took root online, and what led so many social media users to adopt its ideas. In this work, we propose a framework that exploits both social interaction and content signals to uncover evidence of user radicalization or support for QAnon. Leveraging a large dataset of 240M tweets collected in the run-up to the 2020 US Presidential election, we define and validate a multivariate metric of radicalization. We use that to separate users in distinct, naturally-emerging, classes of behaviors associated with radicalization processes, from self-declared QAnon supporters to hyper-active conspiracy promoters. We also analyze the impact of Twitter's moderation policies on the interactions among different classes: we discover aspects of moderation that succeed, yielding a substantial reduction in the endorsement received by hyperactive QAnon accounts. But we also uncover where moderation fails, showing how QAnon content amplifiers are not deterred or affected by the Twitter intervention. Our findings refine our understanding of online radicalization processes, reveal effective and ineffective aspects of moderation, and call for the need to further investigate the role social media play in the spread of conspiracies.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172924","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: 14

We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience 我们在一起:量化社区主观幸福感和弹性

International Conference on Web and Social Media Pub Date : 2022-08-23 DOI: 10.48550/arXiv.2208.10766

MeiXing Dong, Rui Sun, Laura Biester, Rada Mihalcea

{"title":"We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience","authors":"MeiXing Dong, Rui Sun, Laura Biester, Rada Mihalcea","doi":"10.48550/arXiv.2208.10766","DOIUrl":"https://doi.org/10.48550/arXiv.2208.10766","url":null,"abstract":"The COVID-19 pandemic disrupted everyone's life across the world. In this work, we characterize the subjective wellbeing patterns of 112 cities across the United States during the pandemic prior to vaccine availability, as exhibited in subreddits corresponding to the cities. We quantify subjective wellbeing using positive and negative affect. We then measure the pandemic's impact by comparing a community's observed wellbeing with its expected wellbeing, as forecasted by time series models derived from prior to the pandemic. We show that general community traits reflected in language can be predictive of community resilience. We predict how the pandemic would impact the wellbeing of each community based on linguistic and interaction features from normal times before the pandemic. We find that communities with interaction characteristics corresponding to more closely connected users and higher engagement were less likely to be significantly impacted. Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted. Additionally, we use the same features to also predict how quickly each community would recover after the initial onset of the pandemic. We similarly find that communities that talked more about family, affiliations, and identifying as part of a group had a slower recovery.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127722953","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