Proceedings of the 5th International Workshop on Social Media World Sensors最新文献

{"title":"Session details: Paper Session","authors":"C. Schifanella","doi":"10.1145/3362170","DOIUrl":"https://doi.org/10.1145/3362170","url":null,"abstract":"","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124823024","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}

{"title":"Towards Democratizing Social Media Data Analysis and Visualization Using SoMDA","authors":"Antonio Manoel de Lima Neto, João Paulo Clarindo, Fábio Coutinho","doi":"10.1145/3345645.3351103","DOIUrl":"https://doi.org/10.1145/3345645.3351103","url":null,"abstract":"Currently, access to social media is part of the daily lives of most people in the world, continuously generating a huge volume of data. In this context, social media offers the opportunity to obtain information and knowledge discovery, being able to reveal trends in social, economic, political and cultural context. However, the required effort demands the usage of APIs, script programming, server configuration, etc. These tasks usually are restricted to specialized users. In order to democratize the access to the information from social media, this work presents SoMDA, a platform for automating the process of extraction, analysis and visualization of social media data using distributed storage and dynamic visualization techniques. Platform evaluation was performed using Twitter data collected through SoMDA and a usability test applied to SoMDA Dashboard demonstrating positive results.","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132907379","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}

{"title":"Addressing Information Overload through Text Mining across News and Social Media Streams","authors":"G. Poghosyan","doi":"10.1145/3345645.3351105","DOIUrl":"https://doi.org/10.1145/3345645.3351105","url":null,"abstract":"The state-of-the-art in topic detection and tracking, structured summarization and news recommendation has moved to alternative document representations beyond keywords, in an attempt to utilize the available metadata in the form of timestamps, entities, topics, categories, author information, sentiment, political stance. Yet, despite the availability and the advantages of social metadata, only a few methods have attempted to utilize social annotations for document representation beyond social posts. This report briefly introduces the use of social annotations for news in near-real-time settings and answers the question - Are the social annotations useful for tackling single-domain multiple-document tasks in the news domain, such as topic detection and tracking or story structure extraction?","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123899106","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}

{"title":"Capturing Signals of Enthusiasm and Support Towards Social Issues from Twitter","authors":"Shubhanshu Mishra, J. Diesner","doi":"10.1145/3345645.3351104","DOIUrl":"https://doi.org/10.1145/3345645.3351104","url":null,"abstract":"Social media enables organizations to learn what users say about their products online, and to engage with their potential audiences. Social media has also been allowing individual users and the public to signal their enthusiasm, support, or lack thereof for a broad range of topics. In this paper, we analyze the robustness of a prior framework for tagging tweets across the dimensions of enthusiasm (labels: enthusiastic, passive) and support (labels: supportive, non-supportive). We investigate the quality of annotations in a collection of tweets about three topics, namely, cyberbullying, LGBT rights, and Chronic Traumatic Encephalopathy (CTE) in the National Football League. We train models that achieve >70% and 80% F1 score for classifying tweets for enthusiasm and support, respectively. We assess how text-based signals of enthusiasm and support vary depending on the different annotators. Finally, we propose and demonstrate a network analysis-based approach for combining the annotated tweets with account and hashtag mention networks. This step helps to identify top accounts and hashtags related to the considered categories (enthusiasm and support). Our work offers an alternative or supplemental classification schema and prediction model to standard sentiment analysis and stance detection.","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":" 93","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829634","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}

{"title":"Media Bias Characterization in Brazilian Presidential Elections","authors":"A. S. C. Melo, Leandro Balby Marinho, Adriano Veloso","doi":"10.1145/3345645.3351107","DOIUrl":"https://doi.org/10.1145/3345645.3351107","url":null,"abstract":"News media bias is commonly associated with framing information so as to influence readers judgments. It is not rare to find different news outlets reporting the same events under different perspectives with the intention to deliberately influence the reader. For example, making one side's ideological perspective look better than another. This may be an indication of a well known cognitive bias, the framing effect, which states that people may change their judgment based on how the information is presented (or framed). According to a 2017's survey from the Knight Foundation and Gallup, Americans believe that 62% of the news they consume is biased [1]. Still according to the survey, there is a sharp divergence of bias perception across Republicans and Democrats regarding news organizations. This implies that the perception of bias may be affected by whether one agrees (or not) with the ideological leaning (when present) of the news source. How to expose such biases in an automatic fashion from textual content only? One way to do that is by comparing different news outlets on the same stories and look for divergences. In this talk, we present an investigation on news media bias in the context of Brazilian presidential elections by comparing four popular news outlets during three consecutive election years (2010, 2014, and 2018). We analyse the textual content of news stories in search for three kinds of bias: coverage, association, and subjective language. Coverage bias is related to differences in mention rates of candidates and parties. Association bias [2] occurs when, for example, one candidate is associated with a negative concept while another not. Subjective bias [3], has to do with wording that attempts to influence the readers by appealing to emotion, stereotypes, or persuasive language. We perform a thorough analysis on a large scale news data set where several such biases are exposed.","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122400878","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}

{"title":"A Comparative Temporal Analysis of User-Content-Interaction in Social Media","authors":"Jan Hauffa, Georg Groh","doi":"10.1145/3345645.3351102","DOIUrl":"https://doi.org/10.1145/3345645.3351102","url":null,"abstract":"How long does content published via online social networks and media receive attention? We derive a novel definition of content lifetime from the temporal distribution of user-content interactions and apply it to a comparative study of multiple traditional and modern online social media. This characterization of lifetime is not only relevant for learning about human behavior on social media; when designing an experiment on social media data that involves temporal quantization, it can assist in making a principled choice of interval size. We find that across all media, interactions take place on four main time scales: short term activity of at most 15 minutes, medium term activity in the range of one hour and two days, respectively, and long term activity of two weeks or more.","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124303098","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}

{"title":"Session details: Keynote Addresses","authors":"Mario Cataldi","doi":"10.1145/3362169","DOIUrl":"https://doi.org/10.1145/3362169","url":null,"abstract":"","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773807","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}

{"title":"Human Behaviour on the Web: Evolution, Interactions and Exploitation","authors":"L. Vassio","doi":"10.1145/3345645.3351106","DOIUrl":"https://doi.org/10.1145/3345645.3351106","url":null,"abstract":"The Web has a fundamental impact on our life, and its usage is quite dynamic and heterogeneous. Moreover, the Web, and in particular Online Social Networks allow people to communicate directly with the public, bypassing filters of traditional medias. Among the others, politicians and companies are exploiting this technologies to widen their influence. In the talk I will show techniques to capture such usage evolution and analyze people interaction on the Internet. This information allows us to understand how users and web services change over time, and how someone can take advantage of these behaviours. There is a large literature about how to evaluate and influence a social network from an analytic point of view [7]. However, it is often not clear if the hypotheses in the mathematical models are valid in real cases and rarely there is enough ground-truth information in large scale experiments. In practice, we observe in the networks heuristic strategies following a trial-and-error approach and emerging behaviours. This is why I am focusing on capturing the human behaviour, directly measured in the present (and past) Web. Thanks to logs of users' traffic, and by active crawling Online Social Networks, I show how to reconstruct users' online activity and to model their behaviour, thanks also to Machine Learning approaches. We deeply understand the evolution of time spent of the Web by the users and the shifting from static pages to the usage of dynamic user-created pages and content in social networks ([4, 6, 9]). The peculiar social networks and other categories usage and evolution can be seen in [1, 4]. Still, considering a short horizon, usage is repetitive and this can exploited for identifying users even when they are not logged (behavioural fingerprints, [8]). Data from human behaviour can be used for extracting and processing social information, sometimes even without the explicit cooperation of the users, to provide new collaborative services. For example, a new service could be the recommendation of hot news that are obtained from aggregated clicks of entire communities (WeBrowse tool, proposed in [3]). Emerging behaviours of the users can also be exploited for expanding someone's influence. A clear example is the recent political debate in Instagram [5] or in WhatsApp [2]. Results suggest that profiles of politicians are able attract markedly different interactions. Moreover, a small group of very active followers can influence a large portion of the network.","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809247","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}

{"title":"Proceedings of the 5th International Workshop on Social Media World Sensors","authors":"Shubhanshu Mishra, J. Diesner, Fábio Coutinho","doi":"10.1145/3345645","DOIUrl":"https://doi.org/10.1145/3345645","url":null,"abstract":"","PeriodicalId":408440,"journal":{"name":"Proceedings of the 5th International Workshop on Social Media World Sensors","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122618","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}