We present a system for finding, from Twitter data, events that raised the interest of users within a given time period and the important dates for each event. An event is represented by many terms whose frequency increases suddenly at one or more moments during the analysed period. In order to determine the terms (especially the hashtags) dealing with a topic, we propose methods to cluster sim...

Arguably one of the most important features of Twitter is the support for “retweets” or messages re-posted verbatim by a user that were originated by someone else. Despite the fact that retweets are routinely studied and reported, many important questions remain about user motivation for their use and their significance. In this paper we answer the question of what users indicate when they retw...

Sensing social media for trends and events has become possible as increasing number of users rely on social media to share information. In the event of a major disaster or social event, one can therefore study the event quickly by gathering and analyzing social media data. One can also design appropriate responses such as allocating resources to the affected areas, sharing event related informa...

Online social platforms collapse or converge public and private boundaries, creating both opportunities and challenges for pursuing publicity, privacy, and sociality. Presentations of the self thus become networked performances that must convey polysemic content to audiences, actual and imagined, without compromising one’s own sense of self. This study explored how individuals perform the self ...

Datasets extracted from the microblogging service Twitter are often generated using specific query terms or hashtags. We describe how a dataset produced using the query term ‘syria’ can be increased in size to include tweets on the topic of Syria that do not contain that query term. We compare three methods for this task, using the top hashtags from the set as search terms, using a hand selecte...

This paper addresses the issue of how personality recognition can be helpful for sentiment analysis. We exploited the corpus for sentiment analysis released for the SEMEVAL 2013, we automatically annotated personality labels by means of an unsupervised system for personality recognition. We validated the automatic annotation on a small set of Twitter users, whose personality types have been col...

We present a demonstrator that visualizes the Twittersphere debate on whether the UK should remain in or leave the European Union. Data is collected using three strategies: hashtag search terms, extraction from the full stream and following specific users. The demonstrator can be used to show the different discussion topics identified by the different search strategies.

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 hal...

In this paper we describe how Twitter is used in various languages. We observe notable differences between languages regarding the use of hashtags, links, mentions, and conversations. We propose two dimensions that can be used to classify languages, each of which is likely to require different ways of analysis.