WhatsApp, a very popular cross-platform messaging platform with more than one and a half billion users, is the preferred medium for communication in several countries. One of the interesting features in the platform is called Groups (WG), which is an encrypted end-to-end virtual room created by a group of individuals, where only the group members can send and see the messages. The goodness of privacy and security offered by WG have attracted the attention of families, friends, businesses, and organizations. However, in several cases, those WG's have been used as a powerful tool for spreading dangerous content or fake news. In this work, we propose a new method for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages. Our framework creates an ensemble of networks that represent the temporal evolution of the conversation every 10 minutes. In this way, we use network measurements to build an Engagement Index (EI) for fractions of the conversations. Our results in five real-world WGs data indicate that the EI is able to identify different type of conversations, and users' behaviors according to each category, as well as anomalies on users' engagement.
@cite_8: Sentiment analysis in Twitter is a field that has recently attracted research interest. Twitter is one of the most popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. The presented studies are categorized according to the approach they follow. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.
@cite_1: This chapter explores Facebook.com's current and potential uses for increasing college student involvement. Ideas based on Astin's model of student involvement provide a framework for discussion.
@cite_3: Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.
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@cite_12: Social media is a convenient tool for expressing ideas and a powerful means for opinion formation. In this paper, we apply sentiment analysis and machine learning techniques to study a controversial American individual on Twitter., aiming to grasp temporal patterns of opinion changes and the geographical distribution of sentiments positive, neutral or negative, in the American territory. Specifically, we choose the American TV presenter and candidate for the Republican party nomination, Donald J. Trump. The results acquired aim to elucidate some interesting points about the data, such as: what is the distribution of users considering a match between their sentiment and their relevance? Which clusters can we get from the temporal data of each state? How is the distribution of sentiments, before and after, the first two Republican party debates?
@cite_17: This paper, in order to deepen our understanding of the role of opinion leadership on Twitter, the world's largest microblogging service, has investigated the interrelationships between opinion leadership, Twitter use motivations, and political engagement. It finds that Twitter opinion leaders have higher motivations of information seeking, mobilization, and public expression than nonleaders. It has also been found that mobilization and public-expression motivations mediate the association between perceived opinion leadership and Twitter use frequency. Most importantly, this study finds that Twitter opinion leadership makes a significant contribution to individuals' involvement in political processes, while Twitter use itself or media use motivation does not necessarily help individuals' political engagement.
Several works have developed methods to measure the engagement of individuals in social environments, like the discovering of correlations between the use of Facebook and student engagement @cite_1 ; the role of Twitter opinion leadership on individuals’ engagement in politics @cite_17 ; and measuring the engagement of businesses on Twitter and the positive effects on consumers' engagement with online word-of-mouth communication @cite_3 . Another approach, based on content analysis, is focused on studies applying Natural Language Processing (NLP) techniques for processing User Generated Content in Opinion Mining and Sentiment Analysis @cite_8 . Also, based on these techniques, several studies have developed methods to measure the users' engagement on marketing campaigns, products or individuals, over time @cite_12 @cite_13 . However, these previous work employed the content of the messages as input of the methods and are not suitable for the case of encrypted scenario. To the best of our knowledge, this is the first approach that tackle the mining of users' interaction and engagement in encrypted group message and by employing temporal networks.