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.
In the realm of social network analysis @cite_22 @cite_19 @cite_9 @cite_14 @cite_29 @cite_28 @cite_27 @cite_11 @cite_1 , diffusion of information has been an active research area in the social network analysis @cite_16 @cite_18 @cite_23 @cite_3 @cite_4 , however much less is known about spread of ideas in the form of hashtags. Specifically, prediction of adopting hashtags in a given time frame has been virtually an untrodden area yet.
@cite_18 @cite_14 @cite_4 @cite_22 @cite_28 @cite_29 @cite_9 @cite_1 @cite_3 @cite_19 @cite_27 @cite_23 @cite_16 @cite_11
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We present results of network analyses of information diffusion on Twitter, via users’ ongoing social interactions as denoted by “@username” mentions. Incorporating survival analysis, we constructed a novel model to capture the three major properties of information diffusion: speed, scale, and range. On the whole, we find that some properties of the tweets themselves predict greater information propagation but that properties of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors. Implications for end users and system designers are discussed. Modeling the movement of information within social media outlets, like Twitter, is key to understanding to how ideas spread but quantifying such movement runs into several difficulties. Two specific areas that elude a clear characterization are (i) the intrinsic random nature of individuals to potentially adopt and subsequently broadcast a Twitter topic, and (ii) the dissemination of information via non-Twitter sources, such as news outlets and word of mouth, and its impact on Twitter propagation. These distinct yet inter-connected areas must be incorporated to generate a comprehensive model of information diffusion. We propose a bispace model to capture propagation in the union of (exclusively) Twitter and non-Twitter environments. To quantify the stochastic nature of Twitter topic propagation, we combine principles of geometric Brownian motion and traditional network graph theory. We apply Poisson process functions to model information diffusion outside of the Twitter mentions network. We discuss techniques to unify the two sub-models to accurately model information dissemination. We demonstrate the novel application of these techniques on real Twitter datasets related to mass protest adoption in social communities. There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread. Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure. We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads. Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called "tweetonomy", together with a set of stream-based measures that allow researchers to systematically define and compare different stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical findings demonstrate that different social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.