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.
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.
@cite_8 @cite_1 @cite_3 @cite_13 @cite_12 @cite_17
2538660910 2166434810 1634225020 2282408813
The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91 accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95 accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein. Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications. In this paper, we discuss a variety of issues related to opinion mining from microposts, and the challenges they impose on an NLP system, along with an example application we have developed to determine political leanings from a set of pre-election tweets. While there are a number of sentiment analysis tools available which summarise positive, negative and neutral tweets about a given keyword or topic, these tools generally produce poor results, and operate in a fairly simplistic way, using only the presence of certain positive and negative adjectives as indicators, or simple learning techniques which do not work well on short microposts. On the other hand, intelligent tools which work well on movie and customer reviews cannot be used on microposts due to their brevity and lack of context. Our methods make use of a variety of sophisticated NLP techniques in order to extract more meaningful and higher quality opinions, and incorporate extra-linguistic contextual information. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure.