Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07 accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52 and 79.13 respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7 (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.
While retweets and followers support a hashtag's growth, they also paradoxically undermine its persistence. Various researchers have tried to systematically analyze the features that contribute to the growth and stabilization of the hashtags. Yang and Scott @cite_2 examined the roles of relevance'' and exposure'' for hashtag adoption @cite_2 . @cite_8 studied the duality of hashtags as topical identifiers and a symbol of community membership. @cite_17 studied the growth, survival, and context of novel hashtags during the 2012 U.S. presidential debate. They proposed a framework to capture dynamics of hashtags based on their topicality, interactivity, diversity, and prominence.
@cite_8 @cite_17 @cite_2
1551048630 2104894372 2145446394 2171410332
We examine the growth, survival, and context of 256 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. We propose a "conversational vibrancy" framework to capture dynamics of hashtags based on their topicality, interactivity, diversity, and prominence. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between features of this framework and the relative success of hashtags. Specifically, retweets always contribute to faster hashtag adoption, replies extend the life of "winners" while having no effect on "also-rans." This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks. We draw on theories of uses and gratification, organizational ecology, and language evolution to discuss these findings and their implications for understanding social influence and collective action in social media more generally. Researchers and social observers have both believed that hashtags, as a new type of organizational objects of information, play a dual role in online microblogging communities (e.g., Twitter). On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users. Are the real users aware of this dual role of hashtags? Is the dual role affecting their behavior of adopting a hashtag? Is hashtag adoption predictable? We take the initiative to investigate and quantify the effects of the dual role on hashtag adoption. We propose comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities. Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before. 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. Because of Twitter’s popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naive bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.