This study traces the rhythms of news storytelling on Twitter via the #egypt hashtag. Using computational discourse analysis, we examine news values and the form of news exhibited in #egypt from January 25 to February 25, 2011, preand post-resignation of Hosni Mubarak. Results point to a hybridity of old and newer news values, with emphasis on the drama of instantaneity, the crowdsourcing of el...

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level t...

This paper introduces a toolkit used for the purpose of detecting replacements of different grammatical and semantic structures in ongoing text production logged as a chronological series of computer interaction events (so-called keystroke logs). The specific case we use involves human translations where replacements can be indicative of translator behaviour that leads to specific features of t...

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-theart face analysis software to infer gender, age, and race from profile images of 350K Twitter ...

Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a new task proposed for SemEval-2016 Task6, involving predicting stance for a dataset of tweets on the topics of abortion, atheism, climate change, feminism and Hillary Clinton....

We introduce the notion of “seminar users”, who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecti...

Social media play an increasingly important role in political communication. Various studies investigated how individuals adopt social media for political discussion, to share their views about politics and policy, or to mobilize and protest against social issues. Yet, little attention has been devoted to the main actors of political discussions: the politicians. In this paper, we explore the t...

Microblogging applications such as Twitter are experiencing tremendous success. Twitter users use hashtags to categorize posted messages which aim at bringing order to the chaos of the Twittersphere. However, the percentage of messages including hashtags is very small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of ch...

one of the most important issues related to knowledge discovery is the field of comment mining. opinion mining is a tool through which the opinions of people who comment about a specific issue can be evaluated in order to achieve some interesting results. this is a subset of data mining. opinion mining can be improved using the data mining algorithms. one of the important parts of opinion minin...