The ever-increasing usage of Social Medias, like Twitter, have enabled companies and public personalities to communicate and to influence their public. However, how to analyze and measure something as subjective as interpersonal influences? This paper presents a methodology for measuring and to analyze how Twitter's posts (tweets) can influence their readers. In our work, the interpersonal influence of user is given by the capacity of tweets on influencing or affecting positively or negatively his/her mood or state of mind. Hence, the influence of one user A on another user B is the fluctuation of B's mood when he/she reads and retweets a message posted by A. We measured the user's mood using the Subjective Well-Being (SWB) that evaluates the mood of a person based on the positive and negative sentiment pointed on their collection of authored documents, e.g. on his/her thread of tweets. We applied the proposed methodology to analyze the influence of an important Twitter account, Your Holiness the Pope Francis, on the mood of his followers. The results show the existence of the Pope's influence on his followers' mood in a short-term period: from 1 to 2 hours after he/she retweets a Pope's message.

In location-based social networks, such as Foursquare, users may post tips with their opinions about visited places. Tips may directly impact the behavior of future visitors, providing valuable feedback to business owners. Sentiment or polarity detection has attracted great attention due to its vast applicability in opinion summarization, ranking or recommendation. However, the automatic detection of polarity of tips faces challenges due to their short sizes and informal content. This paper presents an empirical study of supervised and unsupervised techniques to detect the polarity of Foursquare tips. We evaluate the effectiveness of four methods on two sets of tips, finding that a simpler lexicon-based approach, which does not require costly manual labeling, can be as effective as state-of-the-art supervised methods. We also find that a hybrid approach that combines all considered methods by means of stacking does not significantly outperform the best individual method.

On Foursquare, one of the currently most popular location-based social networks, users can not only share which places (venues) they visit but also leave short comments (tips) about their previous experiences at specific venues. Tips may provide a valuable feedback for business owners as well as for potential new customers. Sentiment or polarity classification provides useful tools for opinion summarization, which can help both parties to quickly obtain a predominant view of the opinions posted by users at a specific venue. We here present what, to our knowledge, is the first study of polarity of Foursquare tips. We start by characterizing two datasets of collected tips with respect to their textual content. Some inherent characteristics of tips, such as short sizes as well as informal and often noisy content, pose great challenges to polarity detection. We then investigate the effectiveness of four alternative polarity classification strategies on subsets of our dataset. Three of the considered strategies are based on supervised machine learning techniques and the fourth one is an unsupervised lexicon-based approach. Our evaluation indicates that effective polarity classification can be achieved even if the simpler lexicon-based approach, which does not require costly manual tip labeling, is adopted.