Document Based Clustering For Detecting Events in Microblogging Websites

Social media has a great in?uence in our daily lives. People share their opinions, stories, news, and broadcast events using social media. This results in great amounts of information in social media. It is cumbersome to identify and organize the interesting events with this massive volumes of data, typically browsing, searching, monitoring events becomes more and more challenging. A lot of work has been done in the area of topic detection and tracking (TDT). Most of these methods are based on single-modality (e.g., text, images) information or multi-modality information. In the single-modality analysis, many existing methods adopt visual information (e.g., images and videos) or textual information (e.g., names, time references, locations, title, tags, and description) in isolation to model event data for event detection and tracking. This problem can be resolved by a novel multi-model social event tracking and an evolutionary framework not only effectively capturing the events, but also generates the summary of these events over time. We proposed a novel method works with mmETM, which can effectively model the social documents, which includes the long text along with the images. It learns the similarities between the textual and visual modalities to separate the visual and non-visual representative topics. To incorporate our method to social tracking, we adopted an incremental learning technique represented as mmETM, which gives informative textual and visual topics of event in social media with respect to the time. To validate our work, we used a sample data set and conducted various experiments on it. Both subjective and quantitative assessments show that the proposed mmETM technique performs positively against a few best state-of-the art techniques

Comportamientos y temas de conversación en grupos de Facebook destinados a dialogar sobre desastres naturales

La presente investigación aborda el comportamiento y temas de discusión de los grupos de Facebook creados con el objetivo de dialogar sobre desastres naturales. Este trabajo recoge estudios sobre cómo los temas que se discuten en las comunidades online podrían ayudar en prevenir y enfrentar un desastre natural. Además, busca comprender cómo la información es difundida en estos espacios puede perjudicar este trabajo tiene como objetivo contribuir con las investigaciones sobre las percepciones de las personas ante los desastres naturales en el Perú, comprendiendo las preocupaciones y el pensar de las personas, así como los temas sobre desastres naturales que se discuten en una comunidad digital. Asimismo, otro motivo es contribuir con los estudios sobre el comportamiento de usuarios de grupos de Facebook, ahondando en esta oportunidad en los grupos que han sido creados con fines para discutir sobre desastres naturales. Como metodología se empleó una metodología cualitativa en donde se analizarán a los usuarios del grupo de Facebook SISMOS, CATASTROFES Y mas.2020. La presente investigación tiene el objetivo analizar el comportamiento de los grupos de Facebook creados para dialogar sobre desastres naturales. Para esto, se utilizó como recurso diversos estudios sobre el papel de las comunidades online para ayudar a la prevención de desastres naturales tomando en cuentaThis research addresses the behavior and discussion topics of Facebook groups created for the purpose of dialogue on natural disasters. This work gathers studies on how the topics discussed in the online communities could help in preventing and facing a natural disaster. In addition, it seeks to understand how information is disseminated in these spaces can harm this work. The objective of this work is to contribute to research on people's perceptions of natural disasters in Peru, understanding people's concerns and thoughts, as well as the issues of natural disasters that are discussed in a digital community. Likewise, the reason for another contribution is to contribute with studies on the behavior of users of Facebook groups, deepening in this opportunity in the groups that have been created with the purpose of discussing natural disasters. As a methodology we used a qualitative methodology where we will analyze the users of the Facebook group SISMOS, CATASTROPHES AND more.2020.Trabajo de investigació

루머 증폭기 : 소셜 미디어 내 루머 확산에 대한 에코 챔버 영향의 이해

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 권태경.인터넷 상에서 퍼지는 루머들은 소셜 미디어의 확산으로 인해 사람들로부터 더욱 흥미를 끌게 되었다. 본 연구에서는 공통적인 루머에 대해 관심을 가지고 퍼뜨리는 유저 집단을 루머 에코 챔버로 정의함으로써, 소셜 미디어에서 에코 챔버가 루머 전파에 어떤 역할을 하는지 분석하였다. 미국에서 많이 사용되는 6개의 온라인 팩트 체크 사 이트로부터 루머 데이터를 수집하였고, 이를 바탕으로 트위터 API를 통해 162개의 루머에 대해 191,720명이 작성한 310,545개의 트윗, 리트윗을 수집하였다. 먼저 데이터 분석을 통해 루머 에코 챔버를 정의 후, 기존에 연구되어 왔던 정치적인 특성을 가진 에코 챔버와 루머 에코 챔버의 특성을 비교하였다. 루머 에코 챔버는 기존 에코 챔버 연구에서 관찰되던 특성인 선택적 노출(selective exposure), 동종 선호(homophily)를 보여줌으로써 에코 챔버의 특성을 가지고 있음을 확인하였다. 에코 챔버가 루머 전파에 미치는 영향을 분석하기 위해서 리트윗 그래프로 구성된 루머 캐스케이드(cascade) 분석하였고, 루머 에코 챔버가 영향을 미친 루머 캐스케이드가 더 많은 사람에게 더 빠르게 전파되는 것을 관찰하였다. 또한, 에코 챔버 네트워크 분석을 통해 에코 챔버 노드 간 미치는 영향력을 분석하여 노드 간 엣지 수를 기준으로 Top 1%의 허브 에코 챔버들이 전체 루머 케스케이드의 20%에 영향을 주는 것을 알 수 있었다. 이를 통해, 핵심적인 에코 챔버가 루머 전파에 큰 영향을 미치는 것을 알 수 있었다.Spreading rumors on the Internet becomes increasingly interesting due to the proliferation of online social media. To understand how an echo chamber, a group of users who share similar interests or views, plays a role in rumor propagation, we define a rumor echo chamber as a group of users that have participated in propagating common rumors. By collecting and analyzing 162 recent rumors from six popular fact-checking sites, and their associated 310,545 tweets/retweets generated by 191,720 users, we find that (i) users in rumor echo chambers tend to exhibit strong selective exposure and (ii) members of the same rumor echo chamber are likely to share similar political views. We also show that the rumors whose propagation involves echo chamber members tend to be more viral and more quickly propagated than those without echo chamber members. We also model the relations among echo chambers by an echo chamber network, identify the hub echo chambers (in terms of degree), and reveal that the top 1% of hub echo chambers are responsible for 20% of all the rumor cascades, which implies that core rumor echo chambers significantly contribute to rumor spreads.Chapter 1 Introduction 1 Chapter 2 Background 4 2.1 Rumor 4 2.2 Echo Chamber 5 Chapter 3 Related Work 6 3.1 Rumor Propagation 6 3.2 Echo Chambers in Social Media 7 Chapter 4 Data Collection 9 Chapter 5 Rumor Echo Chamber 12 5.1 Definition of Rumor Echo Chamber 12 5.2 Characteristics of A Rumor Echo Chamber 14 5.2.1 Selective Exposure 14 5.2.2 Political Homophily 16 Chapter 6 Effects of Rumor Echo Chambers 18 6.1 Structure of Rumor Cascades 18 6.2 Rumor Propagation Speed 21 Chapter 7 Network of Rumor Echo Chambers 23 7.1 Model of Echo Chamber Network 23 7.2 Hub Echo Chambers 25 Chapter 8 Conclusion 28 Bibliography 29 초록 33Maste

The retransmission of rumor and rumor correction messages on Twitter

This article seeks to examine the relationships among source credibility, message plausibility, message type (rumor or rumor correction) and retransmission of tweets in a rumoring situation. From a total of 5,885 tweets related to the rumored death of the founding father of Singapore Lee Kuan Yew, 357 original tweets without an “RT” prefix were selected and analyzed using negative binomial regression analysis. The results show that source credibility and message plausibility are correlated with retransmission. Also, rumor correction tweets are retweeted more than rumor tweets. Moreover, message type moderates the relationship between source credibility and retransmission as well as that between message plausibility and retransmission. By highlighting some implications for theory and practice, this article concludes with some limitations and suggestions for further research.MOE (Min. of Education, S’pore)Accepted versio

Credibility assessment of financial stock tweets

© 2020 The Authors Social media plays an important role in facilitating conversations and news dissemination. Specifically, Twitter has recently seen use by investors to facilitate discussions surrounding stock exchange-listed companies. Investors depend on timely, credible information being made available in order to make well-informed investment decisions, with credibility being defined as the believability of information. Much work has been done on assessing credibility on Twitter in domains such as politics and natural disaster events, but the work on assessing the credibility of financial statements is scant within the literature. Investments made on apocryphal information could hamper efforts of social media's aim of providing a transparent arena for sharing news and encouraging discussion of stock market events. This paper presents a novel methodology to assess the credibility of financial stock market tweets, which is evaluated by conducting an experiment using tweets pertaining to companies listed on the London Stock Exchange. Three sets of traditional machine learning classifiers (using three different feature sets) are trained using an annotated dataset. We highlight the importance of considering features specific to the domain in which credibility needs to be assessed for – in the case of this paper, financial features. In total, after discarding non-informative features, 34 general features are combined with over 15 novel financial features for training classifiers. Results show that classifiers trained on both general and financial features can yield improved performance than classifiers trained on general features alone, with Random Forest being the top performer, although the Random Forest model requires more features (37) than that of other classifiers (such as K-Nearest Neighbours − 9) to achieve such performance