@misc{_twitterStats,
  title = {{150+ {Amazing} {Twitter} {Statistics}}},
  url = {http://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/},
  abstract = {Updated for November 2015. Here is a comprehensive list of all of the Twitter statistics and facts that you need to know. Included are Twitter brand stats, mobile stats and user demographics.},
  urldate = {2015-11-19TZ},
  journal = {DMR},
}


@article{rosa_topical,
  title = {{Topical {Clustering} of {Tweets}}},
  url = {http://citeseerx.ist.psu.edu/viewdoc/citations;jsessionid=E39EC67ABDA8336113C6B3E40E1890CB?doi=10.1.1.207.4287},
  abstract = {CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In the emerging field of micro-blogging and social communication services, users post millions of short messages every day. Keeping track of all the messages posted by your friends and the conversation as a whole can become tedious or even impossible. In this paper, we presented a study on automatically clustering and classifying Twitter messages, also known as “tweets”, into different categories, inspired by the approaches taken by news aggregating services like Google News. Our results suggest that the clusters produced by traditional unsupervised methods can often be incoherent from a topical perspective, but utilizing a supervised methodology that utilize the hash-tags as indicators of topics produce surprisingly good results. We also offer a discussion on temporal effects of our methodology and training set size considerations. Lastly, we describe a simple method of finding the most representative tweet in a cluster, and provide an analysis of the results.},
  urldate = {2015-11-19TZ},
  author = {Rosa, Kevin Dela and Shah, Rushin and Lin, Bo and Gershman, Anatole and Frederking, Robert},
}


@inproceedings{godin_using_2013,
  address = {Republic and Canton of Geneva, Switzerland},
  series = {{WWW} '13 {Companion}},
  title = {{Using {Topic} {Models} for {Twitter} {Hashtag} {Recommendation}}},
  isbn = {978-1-4503-2038-2},
  url = {http://dl.acm.org/citation.cfm?id=2487788.2488002},
  abstract = {Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags.},
  urldate = {2015-11-19TZ},
  booktitle = {Proceedings of the 22Nd {International} {Conference} on {World} {Wide} {Web}},
  publisher = {International World Wide Web Conferences Steering Committee},
  author = {Godin, Fréderic and Slavkovikj, Viktor and De Neve, Wesley and Schrauwen, Benjamin and Van de Walle, Rik},
  year = {2013},
  keywords = {hashtag prediction, microposts, short-text classification, topic models, twitter},
  pages = {593--596},
}


@inproceedings{umap11,
  author = {Eva Zangerle and Wolfgang Gassler and G\"unther Specht},
  title = {{Recommending \#-tags in Twitter}},
  booktitle = {Proceedings of the Workshop on Semantic Adaptive Social Web 2011 in connection with the 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011},
  address = {Gerona, Spain},
  publisher = {CEUR-WS.org, ISSN 1613-0073, Vol. 730, available online at http://ceur-ws.org/Vol-730/paper7.pdf, urn:nbn:de:0074-581-7},
  year = {2011},
  pages = {67-78},
}


@article{poschko_exploring_2011,
  title = {{Exploring {Twitter} {Hashtags}}},
  url = {http://arxiv.org/abs/1111.6553},
  abstract = {Twitter messages often contain so-called hashtags to denote keywords related to them. Using a dataset of 29 million messages, I explore relations among these hashtags with respect to co-occurrences. Furthermore, I present an attempt to classify hashtags into five intuitive classes, using a machine-learning approach. The overall outcome is an interactive Web application to explore Twitter hashtags.},
  urldate = {2015-11-19TZ},
  journal = {arXiv:1111.6553 [cs]},
  author = {Pöschko, Jan},
  month = {nov},
  year = {2011},
  note = {arXiv: 1111.6553},
  keywords = {Computer Science - Computation and Language},
}


@inproceedings{li2011twitter,
  title = {{Twitter hash tag prediction algorithm}},
  author = {Li, Tianxi and Wu, Yu and Zhang, Yu},
  booktitle = {ICOMP’11-The 2011 International Conference on Internet Computing},
  year = {2011},
}


@inproceedings{davidov2010enhanced,
  title = {{Enhanced sentiment learning using twitter hashtags and smileys}},
  author = {Davidov, Dmitry and Tsur, Oren and Rappoport, Ari},
  booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics: Posters},
  pages = {241--249},
  year = {2010},
  organization = {Association for Computational Linguistics},
}


@article{go2009twitter,
  title = {{Twitter sentiment classification using distant supervision}},
  author = {Go, Alec and Bhayani, Richa and Huang, Lei},
  journal = {CS224N Project Report, Stanford},
  volume = {1},
  pages = {12},
  year = {2009},
}


@misc{bagofwords,
  title = {{Bag-of-words model}},
  copyright = {Creative Commons Attribution-ShareAlike License},
  url = {https://en.wikipedia.org/w/index.php?title=Bag-of-words_model&oldid=680036687},
  abstract = {The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. Recently, the bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification, where the (frequency of) occurrence of each word is used as a feature for training a classifier. An early reference to bag of words in a linguistic context can be found in Zellig Harris's 1954 article on Distributional Structure.},
  language = {en},
  urldate = {2015-12-06TZ},
  journal = {Wikipedia, the free encyclopedia},
  month = {sep},
  year = {2015},
  note = {Page Version ID: 680036687},
}


@misc{trend,
  title = {{To {Trend} or {Not} to {Trend}...}},
  url = {https://blog.twitter.com/2010/to-trend-or-not-to-trend},
  abstract = {Since Twitter first introduced the Trends feature in the summer of 2008, one frequently asked question has been “Why isn’t X trending?” This question has come up around a variety of subjects,...},
  urldate = {2015-12-06TZ},
  journal = {Twitter Blogs},
}