Information diffusion in online social networks

International audienceOnline social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyze this phenomenon. Analyzing information diffusion proves to be a challenging task since the raw data produced by users of these networks are a flood of ideas, recommendations, opinions, etc. The aim of this PhD work is to help in the understanding of this phenomenon. So far, our contributions are the following: (i) a survey of developments in the field; (ii) T-BaSIC, a graph-based model for information diffusion prediction; (iii) SONDY, an open source platform that helps understanding social network users' interests and activity by providing emerging topics and events detection as well as network analysis functionalities

Audience Acquisition in Online Resource Mobilization: Quantifying the Relationship with Influential Actors

The literature recognizes the potential of social media as an alternative channel for promoting the causes of social movements, but little is known about the factors that act on the endeavors by social movement organizations to expand their base of sup-porters in social media environments. Drawing on theories of resource mobilization and social networks, we present a framework for studying how the presence of in-fluential actors or users with a high number of followers in online social networks, can influence the acquisition of new followers by a social movement organization. We collected Twitter data from a major organization in the global justice movement and found evidence that suggests a relationship exists between influential actors and the audience acquired by the social movement organization. However, our model suggests this relationship dwindles as the popularity or number of followers of in-fluential actors reaches extreme large values. Our study sheds light for addressing future questions concerning the factors that drive the propagation of ideologies in social media environments raised by actors of social movements and other social figures in their efforts for bringing about social change

Finding influential users of web event in social media

Users of social media have different influences on the evolution of a Web event. Finding influential users could benefit such information services as recommendation and market analysis. However, most of the existing methods are only based on social networks of users or user behaviors while the role of the contents contributed by users in social media is ignored. In fact, a Web event evolves with both user behaviors and the contents. This paper proposes an approach to find influential users by extracting user behavior network and association network of words within the contents and then uses PageRank algorithm and HITS algorithm to calculate the influence of users on the integration of two networks. The proposed approach is effective on several real-world datasets

Do Influencers Influence? -- Analyzing Players' Activity in an Online Multiplayer Game

In social and online media, influencers have traditionally been understood as highly visible individuals. Recent outcomes suggest that people are likely to mimic influencers' behavior, which can be exploited, for instance, in marketing strategies. Also in the Games User Research field, the interest in studying player social networks has emerged due to the heavy reliance on online influencers in marketing campaigns for games, as well as in keeping players engaged. Despite the inherent value of those individuals, it is still difficult to identify influencers, as the definition of influencers is a debated topic. Thus, how can we identify influencers, and are they indeed the individuals impacting others' behavior? In this work, we focus on influence in retention to verify whether central players impacted others' permanence in the game. We identified the central players in the social network built from the competitive player-vs-player (PvP) multiplayer (Crucible) matches in the online shooter Destiny. Then, we computed influence scores for each player evaluating the increase in similarity over time between two connected individuals. In this paper, we were able to show the first indications that the traditional metrics for influencers do not necessarily apply for games. On the contrary, we found that the group of central players was distinct from the group of influential players, defined as the individuals with the highest influence scores. Then, we provide an analysis of the two groups.Comment: accepted for publication in IEEE Conference on Games (CoG) 202

An Approach for Time-aware Domain-based Analysis of Users Trustworthiness in Big Social Data

In Online Social Networks (OSNs) there is a need for better understanding of social trust in order to improve the analysis process and mining credibility of social media data. Given the open environment and fewer restrictions associated with OSNs, the medium allows legitimate users as well as spammers to publish their content. Hence, it is essential to measure users’ credibility in various domains and accordingly define influential users in a particular domain(s). Most of the existing approaches of trustworthiness evaluation of users in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users’ influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present an approach to analysing domain-based user’s trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user’s content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated by considering temporal factor. We show the accuracy of the proposed algorithm by providing a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs using big data Infrastructure

BOTS SOCIAIS: COMO ROBÔS PODEM SE TORNAR INFLUENTES NO TWITTER

Systems like Klout and Twitalyzer were developed as an attempt to measure the influence of users within social networks. Although the algorithms used by these systems are not publicly known, they have been widely used to rank users according to their influence in the Twitter social network. As media companies might base their viral marketing campaigns on influence scores, in this paper, we investigate if these systems are vulnerable and easy to manipulate. Our approach consists of developing Twitter robot accounts able to interact with real users in order to verify strategies that can increase their influence scores according to different systems. Our results show that it is possible to become influential using very simple strategies, suggesting that these systems should review their influence score algorithms to avoid being tricked by automatic activity.Sistemas como Klout e Twitalyzer foram desenvolvidos em uma tentativa de medir a influência de usuários nas redes sociais. Embora os algoritmos utilizados por esses sistemas não sejam de conhecimento público, eles têm sido amplamente utilizados para ranquear usuários, de acordo com sua influência no Twitter. Como as empresas de mídia baseiam suas campanhas de marketing viral na pontuação de influência, neste trabalho, investigamos se esses sistemas são vulneráveis e fáceis de manipular. Nossa abordagem consiste no desenvolvimento de contas robôs no Twitter, capazes de interagir com usuários reais, a fim de verificar estratégias que podem aumentar sua pontuação de influência, nos diferentes sistemas. Nossos resultados mostram que é possível se tornar influente por meio de estratégias muito simples, o que sugere que esses sistemas devem rever os seus algoritmos de pontuação de influência para evitar atribuir alto grau de influência a contas que possuem atividade automática

Influential spreaders in the political Twitter sphere of the 2013 Malaysian general election

Purpose – The article investigates political influential spreaders in Twitter at the juncture before and after the Malaysian General Election in 2013 (MGE2013) for the purpose of understanding if the political sphere within Twitter reflects the intentions, popularity and influence of political figures in the year in which Malaysia has its first ‘social media election’. Design/methodology/approach – A Big Data approach was used for acquiring a series of longitudinal data sets during the election period. The work differs from existing methods focusing on the general statistics of the number of followers, supporters, sentiment analysis and etc. A retweeting network has been extracted from tweets and retweets and has been mapped to a novel information flow and propagation network we developed. We conducted quantitative studies using k-shell decomposition, which enables the construction of a quantitative Twitter political propagation sphere where members posited at the core areas are more influential than those in the outer circles and periphery. Findings – We conducted a comparative study of the influential members of Twitter political propagation sphere on the election day and the day after. We found that representatives of political parties which are located at the center of the propagation network are winners of the presidential election. This may indicate that influential power within Twitter is positively related to the final election results, at least in MGE2013. Furthermore, a number of non-politicians located at the center of the propagation network also significantly influenced the election. Research limitations/implications – This research is based on a large electoral campaign in a specific election period, and within a predefined nation. While the result is significant and meaningful, more case studies are needed for generalised application for identifying potential winning candidates in future social-media fueled political elections. Practical implications – We presented a simple yet effective model for identifying influential spreaders in the Twitter political sphere. The application of our approach yielded the conclusion that online ‘coreness’ score has significant influence to the final offline electoral results. This presents great opportunities for applying our novel methodology in the upcoming Malaysian General Election in 2018. The discovery presented here can be used for understanding how different players of political parties engage themselves in the election game in Twitter. Our approach can also be adopted as a factor of influence for offline electoral activities. The conception of a quantitative approach in electoral results greatly influenced by social media means that comparative studies could be made in future elections. Originality/value – Existing works related to general elections of various nations have either bypassed or ignored the subtle links between online and offline influential propagations. The modeling of influence from social media using a longitudinal and multilayered approach is also rarely studied. This simple yet effective method provides a new perspective of practice for understanding how different players behave and mutually shape each other over time in the election game