Twitter, a microblogging service, has evolved into a powerful communication platform with millions of active users who generate immense volume of microposts on a daily basis. To facilitate effective categorization and easy search, users adopt hashtags, keywords or phrases preceded by hash (#) character. Successful prediction of the spread and propagation of information in the form of trending topics or hashtags in Twitter, could help real time identification of new trends and thus improve marketing efforts. Social theories such as consistency theory suggest that people prefer harmony or consistency in their thoughts. In Twitter, for example, users are more likely to adopt the same trending hashtag multiple times before it eventually dies. In this paper, we propose a low-rank weighted matrix factorization approach to model trending hashtag adoption in Twitter based on consistency theory. In particular, we first cast the problem of modeling trending hashtag adoption into an optimization problem, then integrate consistency theory into it as a regularization term and finally leverage widely used matrix factorization to solve the optimization. Empirical experiments demonstrate that our method outperforms other baselines in predicting whether a specific trending hashtag will be used by users in future.
Most traditional works have focused on the structure and topology of the social graph. They try to maximize the spread of information and thus marketing profits by detecting the influential nodes and then leveraging viral marketing and social recommendation networks @cite_5 @cite_15 @cite_0 @cite_17 @cite_2 @cite_21 . Meanwhile some woks have modeled the temporal dynamics of information spread @cite_13 @cite_7 @cite_4 @cite_10 . In particular, @cite_10 studies the ways network structure reacts to users posting and sharing contents. Few have used collaborative filtering to predict the probability of a tweet to get retweeted @cite_24 . Some tasks such as profiling users based on substance, style, status and social tendency has been addressed by using tweet content @cite_8 .
@cite_4 @cite_7 @cite_8 @cite_10 @cite_21 @cite_0 @cite_24 @cite_2 @cite_5 @cite_15 @cite_13 @cite_17
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The identification of popular and important topics discussed in social networks is crucial for a better understanding of societ al concerns. It is also useful for users to stay on top of trends without having to sift through vast amounts of shared information. Trend detection methods introduced so far have not used the network topology and has thus not been able to distinguish viral topics from topics that are diffused mostly through the news media. To address this gap, we propose two novel structural trend definitions we call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively. Our analyses and experiments show that structural trends are significantly different from traditional trends and provide new insights into the way people share information online. We also propose a sampling technique for structural trend detection and prove that the solution yields in a gain in efficiency and is within an acceptable error bound. Experiments performed on a Twitter data set of 41.7 million nodes and 417 million posts show that even with a sampling rate of 0.005, the average precision is 0.93 for coordinated trends and 1 for uncoordinated trends. Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion. We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30 ), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of 15-20 . By increasing the popularity of social networking websites like Facebook and Twitter, analysis of the structure of these networks receives significant attentions. The most important part of these analyses is towards detecting communities. The aforementioned structures are known with extremely high inter-connections versus few intra-connections in the graphs. In this paper, we have addressed the community detection problem by a novel framework based on Information Diffusion Model and Shapley Value Concept. Here, each node of the underlying graph is attributed to a rational agent trying to maximize its Shapley Value in the form of information it receives. Nash equilibrium of the game corresponds to the community structure of the graph. Compared with other methods, our approach demonstrates promising results on the well-known real world and synthetic graphs. Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error. Our algorithm is evaluated on Twitter hashtags extracted from a dataset of more than 400 million tweets. We analyze the contribution and the limitations of the various feature types to the spread of information, demonstrating that content aspects can be used as strong predictors thus should not be disregarded. We also study the dependencies between global features such as graph topology and content features.