With the development of social media and online forums, users have grown accustomed to expressing their agreement and disagreement via short texts. Elements that reveal the user’s stance or subjectivity thus becomes an important resource in identifying the user’s position on a given topic

As online social media applications continue to gain its popularity, concerns about the rapid proliferation of Web spam has grown in recent years. These applications allow spammers to submit links anonymously, diverting unsuspected users to spam Web sites. This paper presents a novel co-classification

Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible

of teaching their robot everything they know. The ability of a robot to use various resources in its environment will help its learning capabilities to be self-guided as well as independent. We investigate the use of social media crowdsourcing to allow a bot to access the huge information volume in gathering

Organisations — including insurgent movements — harness social media to engage potential consumers. They evoke sympathetic (and antipathic) response; content sharing engenders affinity and community. We report on a pilot study of presumed rhetorical intent for statuses retweeted by a set

to sampling and missing data. It has many applications, including automatic spam-detection, trend identification, trust management, user-modeling, so-cial search and content classification on online social media.

Abstract We present a large-scale mood analysis in social media texts. We organize the paper in three parts: 1) addressing the problem of feature selection and classifi-cation of mood in blogosphere, 2) we extract global mood patterns at different level of aggregation from a large-scale dataset

the information pertains. Several forms of high-warrant information have been nominated that meet this classification. Information gained about someone through others in that person's social network is one example. People try to find out about one another via common nodes in overlapping social networks

feedback. Of course, despite all this help, we take full responsibility for our analysis and conclusions. such as social media, blogging, video games, and smart phones (see Engagement with new media has the potential to strengthen young people's participation in civic and political life. Educators

Different demographics, e.g., gender or age, can demonstrate substantial variation in their language use, particularly in informal contexts such as social media. In this paper we focus on learning gender differences in the use of sub-jective language in English, Spanish, and Rus-sian Twitter data