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.
* Language use in social media There have been considerable works that focus on the content and its linguistic aspects in social media. Honeycutt and Herring @cite_50 analyzed conversational exchanges in Twitter focusing on mentions. @cite_52 developed an unsupervised learning approach to identify conversational structure from open-topic conversations. Danescu-Niculescu- @cite_6 studied how people adopt linguistic styles while in conversation on Twitter. @cite_3 studied the role of geography and demographics on the language in Twitter. @cite_31 investigated the cultural differences in Twitter's language. @cite_16 studied the characterization of linguistic and psycholinguistic aspects in Twitter. @cite_48 studied how people curse each other in Twitter. @cite_32 performed a large scale quantitative analysis on deleted tweets.
@cite_48 @cite_52 @cite_3 @cite_6 @cite_32 @cite_50 @cite_31 @cite_16
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Cursing is not uncommon during conversations in the physical world: 0.5 to 0.7 of all the words we speak are curse words, given that 1 of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding cursing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing. We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium. The rapid growth of geotagged social media raises new computational possibilities for investigating geographic linguistic variation. In this paper, we present a multi-level generative model that reasons jointly about latent topics and geographical regions. High-level topics such as "sports" or "entertainment" are rendered differently in each geographic region, revealing topic-specific regional distinctions. Applied to a new dataset of geotagged microblogs, our model recovers coherent topics and their regional variants, while identifying geographic areas of linguistic consistency. The model also enables prediction of an author's geographic location from raw text, outperforming both text regression and supervised topic models. The psycholinguistic theory of communication accommodation accounts for the general observation that participants in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its almost forty years of existence, this theory has been empirically supported exclusively through small-scale or controlled laboratory studies. Here we address this phenomenon in the context of Twitter conversations. Undoubtedly, this setting is unlike any other in which accommodation was observed and, thus, challenging to the theory. Its novelty comes not only from its size, but also from the non real-time nature of conversations, from the 140 character length restriction, from the wide variety of social relation types, and from a design that was initially not geared towards conversation at all. Given such constraints, it is not clear a priori whether accommodation is robust enough to occur given the constraints of this new environment. To investigate this, we develop a probabilistic framework that can model accommodation and measure its effects. We apply it to a large Twitter conversational dataset specifically developed for this task. This is the first time the hypothesis of linguistic style accommodation has been examined (and verified) in a large scale, real world setting. Furthermore, when investigating concepts such as stylistic influence and symmetry of accommodation, we discover a complexity of the phenomenon which was never observed before. We also explore the potential relation between stylistic influence and network features commonly associated with social status. This paper describes an empirical study of 1.6M deleted tweets collected over a continuous one-week period from a set of 292K Twitter users. We examine several aggregate properties of deleted tweets, including their connections to other tweets (e.g., whether they are replies or retweets), the clients used to produce them, temporal aspects of deletion, and the presence of geotagging information. Some significant differences were discovered between the two collections, namely in the clients used to post them, their conversational aspects, the sentiment vocabulary present in them, and the days of the week they were posted. However, in other dimensions for which analysis was possible, no substantial differences were found. Finally, we discuss some ramifications of this work for understanding Twitter usage and management of one's privacy. The microblogging service Twitter is in the process of being appropriated for conversational interaction and is starting to be used for collaboration, as well. In order to determine how well Twitter supports user-touser exchanges, what people are using Twitter for, and what usage or design modifications would make it (more) usable as a tool for collaboration, this study analyzes a corpus of naturally-occurring public Twitter messages (tweets), focusing on the functions and uses of the @ sign and the coherence of exchanges. The findings reveal a surprising degree of conversationality, facilitated especially by the use of @ as a marker of addressivity, and shed light on the limitations of Twitters current design for collaborative use. Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51 ). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39 of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools. Twitter has become the de facto information sharing and communication platform. Given the factors that influence language on Twitter ‐ size limitation as well as communication and content-sharing mechanisms ‐ there is a continuing debate about the position of Twitter’s language in the spectrum of language on various established mediums. These include SMS and chat on the one hand (size limitations) and email (communication), blogs and newspapers (content sharing) on the other. To provide a way of determining this, we propose a computational framework that offers insights into the linguistic style of all these mediums. Our framework consists of two parts. The first part builds upon a set of linguistic features to quantify the language of a given medium. The second part introduces a flexible factorization framework, SOCLIN, which conducts a psycholinguistic analysis of a given medium with the help of an external cognitive and affective knowledge base. Applying this analytical framework to various corpora from several major mediums, we gather statistics in order to compare the linguistics of Twitter with these other mediums via a quantitative comparative study. We present several key insights: (1) Twitter’s language is surprisingly more conservative, and less informal than SMS and online chat; (2) Twitter users appear to be developing linguistically unique styles; (3) Twitter’s usage of temporal references is similar to SMS and chat; and (4) Twitter has less variation of affect than other more formal mediums. The language of Twitter can thus be seen as a projection of a more formal register into a size-restricted space.