Community Detection over Social Media: A Compressive Survey

Social media mining is an emerging field with a lot of research areas such as, sentiment analysis, link prediction, spammer detection, and community detection. In today’s scenario, researchers are working in the area of community detection and sentiment analysis because the main component of social media is user. Users create different types of community in social world. The ideas and discussions in the community may be negative or positive. To detect the communities and their behavior researcher have done a lot of work, but still two major issues are presents per survey, Scalability and Quality of the community. These issues of community detection motivate to work in this area of social media mining. This paper gives a bird eye view over social media and community detection

Online Friendships and the Bird’s Nest Drawing in the Age of the Internet

This study was a qualitative exploration of friendships facilitated through the internet and online video games. The goal was to investigate how online friendships compare to in-person friendships in terms of quality. Three English-speaking participants who played an online video game and had an online friendship provided unique case studies describing the differences between an online and in-person friendship. The Bird Nest Drawing art assessment by Kaiser (1996; 2016) revealed themes of attachment security which helped explain the variations in the friendships. The findings of this study opened the topic of online friendships for further exploration in the field of art therapy, both in research and in a therapy setting

Does woman + a network = career progression?

Question: I am an ambitious and talented junior manager who has recently been hired by FAB plc, a large multinational company. I am also a woman and, as part of my induction pack, have received an invitation to join FABFemmes - the in-company women's network. I don't think my gender has been an obstacle to my success thus far and so I don't really feel the need to join. But on the other hand I don't want to turn my back on something that might offer me a useful source of contacts to help me advance up the career ladder. What would be the best thing to do? - Ms Ambitious, UK

Detecting Friendship Within Dynamic Online Interaction Networks

In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Recovering these hidden ties, particularly for casual users who are relatively less active, would enable a wide variety of friendship-aware applications in domains where labeled data are often unavailable, including online advertising and national security. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that the accuracy of many features improves as more data accumulates, and cooperative features are generally reliable. However, periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.Comment: To Appear at the 7th International AAAI Conference on Weblogs and Social Media (ICWSM '13), 11 pages, 1 table, 6 figure

Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions

In this work we study the coupled dynamics of social balance and opinion formation. We propose a model where agents form opinions under bounded confidence, but only considering the opinions of their friends. The signs of social ties -friendships and enmities- evolve seeking for social balance, taking into account how similar agents' opinions are. We consider both the case where opinions have one and two dimensions. We find that our dynamics produces the segregation of agents into two cliques, with the opinions of agents in one clique differing from those in the other. Depending on the level of bounded confidence, the dynamics can produce either consensus of opinions within each clique or the coexistence of several opinion clusters in a clique. For the uni-dimensional case, the opinions in one clique are all below the opinions in the other clique, hence defining a "left clique" and a "right clique". In the two-dimensional case, our numerical results suggest that the two cliques are separated by a hyperplane in the opinion space. We also show that the phenomenon of unidimensional opinions identified by DeMarzo, Vayanos and Zwiebel (Q J Econ 2003) extends partially to our dynamics. Finally, in the context of politics, we comment about the possible relation of our results to the fragmentation of an ideology and the emergence of new political parties.Comment: 8 figures, PLoS ONE 11(10): e0164323, 201

Social Media Sentiment Contagion

We propose an empirical setting to discover sentiment contagion in social media. We find that, after controlling for concurrent events, sentiment contagion exists in social media. We conduct additional analyses to explore how the source and valence of exposure contents and individual heterogeneity affect the degree of sentiment contagion. We find robust evidence of sentiment contagion not only in contents under the same thread but also under different threads of the same forum. Additional analysis provides evidence of negativity bias. In terms of individual heterogeneity, we find that more experienced social media users are less sensitive to sentiments in social media. Last, we find that social media users are more likely to become inactive in the long run after being exposed to more negative contents. Managerial and practical implications are discussed