This paper addresses the task of user gender classification in social media, with an application to Twitter. The approach automatically predicts gender by leveraging observable information such as the tweet behavior, linguistic content of the user’s Twitter feed and the celebrities followed by the user. This paper first evaluates linguistic content based features using LIWC dictionary and popul...

В современных реалиях возникает конкретная потребность в высококвалифицированных специалистах области новейших технологий, охраны окружающей среды, экономики и бизнеса, которые бы владели языком общения, наиболее распространенной мировом обществе. Для успешного развития обеспечения конкурентоспособности на международных рынках нужны не просто переводчики, а специалисты различных областях науки,...

What makes a tweet worth sharing? We study the content of tweets to uncover linguistic tendencies of shared microblog posts (retweets), by examining surface linguistic features, deeper parse-based features and Twitterspecific conventions in tweet content. We show how these features correlate with a functional classification of tweets, thereby categorizing people’s writing styles based on their ...

Language transfer, the characteristic second language usage patterns caused by native language interference, is investigated by Second Language Acquisition (SLA) researchers seeking to find overused and underused linguistic features. In this paper we develop and present a methodology for deriving ranked lists of such features. Using very large learner data, we show our method’s ability to find ...

With the spread of Internet technologies, use social media has increased exponentially. Although many benefits, it become primary source disinformation or fake news. The news is creating societal and economic issues. It very critical to develop an effective method detect so that can be stopped, removed flagged before spreading. To address challenge accurately detecting news, this paper proposes...