Aggregating Content and Network Information to Curate Twitter User Lists

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process

Social Networks Influence Analysis

Pew Research Center estimates that as of 2014, 74% of the Internet Users used social media, i.e., more than 2.4 billion users. With the growing popularity of social media where Internet users exchange their opinions on many things including their daily life encounters, it is not surprising that many organizations are interested in learning what users say about their products and services. To be able to play a proactive role in steering what user’s say, many organizations have engaged in efforts aiming at identifying efficient ways of marketing certain products and services, and making sure user reviews are somewhat favorable. Favorable reviews are typically achieved through identifying users on social networks who have a strong influence power over a large number of other users, i.e. influential users. Twitter has emerged as one of the prominent social network services with 320 million monthly active users worldwide. Based on the literature, influential Twitter users have been typically analyzed using the following three models: topic-based model, topology-based model, and user characteristics-based model. The topology-based model is criticized for being static, i.e., it does not adapt to the social network changes such as user’s new posts, or new relationships. The user characteristics-based model was presented as an alternative approach; however, it was criticized for discounting the impact of interactions between users, and users’ interests. Lastly, the topic-based model, while sensitive to users’ interests, typically suffers from ignoring the inclusion of inter-user interactions. This thesis research introduces a dynamic, comprehensive and topic-sensitive approach for identifying social network influencers leveraging the strengths of the aforementioned models. Three separate experiments were conducted to evaluate the new approach using the information diffusion measure. In these experiments, software was developed to capture users’ tweets pertinent to a topic over a period of time, and store the tweet’s metadata in a relational database. A graph representing users was extracted from the database. The new approach was applied to the users’ graph to compute an influence score for each user. Results show that the new composite influence score is more accurate in comprehensively identifying true influential users, when compared to scores calculated using the characteristics-based, topic-based, and topology-based models. Also, this research shows that the new approach could leverage a variety of machine learning algorithms to accurately identify influencers. Last, while the focus of this research was on Twitter, our approach may be applicable to other social networks and micro-blogging services

Improving the performance of social media campaigns with the internet of things

This dissertation describes the use of the Internet of Things (IoT) in a social networked media campaign for a digital marketing agency. An electronic object platform a van, the interior of which changed color as a function of Twitter user interactions, was developed to simultaneously motivate and assess engagement with the campaign. The campaign’s dissemination was furthermore supported by Facebook posts and prior IoT based research measuring the popularity of different keywords in Twitter conversations, which were subsequently used as Hashtags in the campaign. Using a quasi-experimental methodology, it was created two campaigns. The results obtained a preview campaign (Without IoT) was compare with the results of the IoT campaign. Under the data analysis the IoT generate positive results as a tool for the Propagation of information.Esta tese explica descreve o uso de internet das coisas (Internet das coisas) em uma campanha Twitter usando com a empresa Van Marketing Digital (VAN) como um patrocinador. Foi desenvolvida uma plataforma de objeto eletrônico por medição da interação na rede social. Esta ferramenta obtido o número de menções, o número de utilizador e as descrições deles no Twitter. O objeto IoT selecionado foi uma Van, que muda a cor das luzes da janela pelo Twitter interação. O carro foi o único incentivo para o público Twitter a fim de analisar se a retribuição virtual foi o suficiente para eles. A campanha começou com uma campanha no Facebook na página de fãs Van, chamando a atenção dos usuários do Facebook como incentivo para participar na campanha Twitter, como resultado IoT potencializada campanha aumentando o alcance es interação de Van marketing nos meios de comunicação social. Os resultados obtidos de uma campanha de pré-visualização (Sem IoT) foi comparar com os resultados da campanha de Internet das coisas. Sob a análise de dados da Internet das coisas gerarem resultados positivos como uma ferramenta para a propagação de informaçõe

Diffusion of Falsehoods on Social Media

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher for fake news, novel tweets, and tweets with negative sentiment and lower lexical structure. In addition, our results show that the impacts of sentiment are opposite for fake news versus real news. We also find that tweets on the environment have a lower retweet count than the baseline religious news and real social news tweets are shared more often than fake social news. Furthermore, our studies show the counter intuitive nature of current correction endeavors by FEMA and other fact checking organizations in combating falsehoods. Specifically, we show that even though fake news causes an increase in correction messages, they influenced the propagation of falsehoods. Finally our empirical results reveal that correction messages, positive tweets and emotionally charged tweets morph faster. Furthermore, we show that tweets with positive sentiment or are emotionally charged morph faster over time. Word count and past morphing history also positively affect morphing behavior

Continue playing: examining language change in discourse about binge-watching on Twitter

2021 Spring.Includes bibliographical references.Utilizing data from Twitter, this study characterized the change in the use of the term binge and its variants from 2009-2019. While there is a significant amount of literature looking at either language change or digital media, this research considered the two as inextricable forces on each other. To examine this and the proposed research questions, a textual analysis was conducted of tweets containing the word binge. Overall, the findings suggest that the December 2013 press release published by Netflix deeming binge-watching as the "new normal" in media consumption, may have pushed binge-watching into the mainstream lexicon. Language use about binge-watching was typically positively connotated in contrast to the negative connotations associated with binge-eating and binge-drinking. The connotative change appears to align with a widening of the definition of "watch" to account for the normality of binge-watching. As the use of binge-watching spread throughout the United States, the pattern of the geographic diffusion of binge-watching did not follow traditional theories of the diffusion of language change. The difference in spread may derive from the corporate origins of the term. Lastly, Twitter enabled and reinforced the spread of binge-watching through the facilitation of the social aspect of binge-watching. The findings of this study provide rich ground for future study

"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams

The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social Computing co-located with ICDM, 2018 Singapor

The Effects of Twitter Posts Regarding COVID-19 Information on the Viewer’s Perception of Credibility: A Study on Misinformation

In the current age, social media plays a large role in daily life. Although there have been massive advancements through social media, there have also been disadvantages due to its prominence. One such disadvantage is the dissemination of misinformation through social media platforms, especially during the COVID-19 pandemic. There are many aspects affecting the social media posts we find credible. This study investigated the different evaluations of COVID-19 credibility ratings in groups with varying political affiliations (conservative and liberal) and primary news sources (social media and newspaper). Misinformative posts along with true posts were used to garner credibility ratings and COVID literacy scores were gathered based on the KAP (knowledge, attitudes, and practices) towards COVID scale. The study had two major hypotheses: 1) There will be a difference between groups of those who classify themselves as politically liberal and those who classify themselves as politically conservative. 2) Those who use social media as their main source of news will rank the misinformative posts with higher credibility than those who use the newspaper as their main source of news. The results indicated no difference between groups with differing news sources. However, there was a difference between groups based on their political affiliations (p=\u3c.001). The results indicated continuity with other current research regarding the impact of social media misinformation

Investigating Heat Risk Messaging Using Social Media Studies and a Survey Experiment

Extreme heat causes hundreds of deaths each year in the United States even though cost-effective protective measures are available. Heat warning messages sent by government agencies have the potential to reduce the negative impacts by motivating people to take protective actions. To help reach the potential, this dissertation examined the content of warning messages and public responses to warning messages in the US. This research analyzed three kinds of data: 1) heat warning messages posted on Twitter, 2) public comments on heat warning messages posted on Facebook, and 3) experimental results collected using an online survey. Results show that, for heat warning messages posted on Twitter, most messages mentioned temperatures and/or Heat Index. Half of messages mentioned heat-safety tips. Less than one-third of messages mentioned heat-health impacts and people’s vulnerability (who is at risk and/or which behavior is at risk). For these four types of mentions, heat warning messages that mentioned more types were retweeted more frequently. In addition, compared to listing specific vulnerable subgroups such as older adults, a statement that “anyone can be at risk” appears to be more effective in making heat warning messages personally relevant to the public. The research also shows that Facebook comments on heat warning messages can suggest people’s needs for risk messaging. The findings can inform researchers and practitioners of how to better communicate risks in the context of extreme heat and other natural hazards