Marco Furini

Bio: Marco Furini is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Social media & The Internet. The author has an hindex of 20, co-authored 100 publications receiving 3493 citations. Previous affiliations of Marco Furini include University of Eastern Piedmont & University of Bologna.

Users Behavior in Location-Aware Services: Digital Natives versus Digital Immigrants

Abstract: Location-aware services may expose users to privacy risks as they usually attach user’s location to the generated contents. Different studies have focused on privacy in location-aware services, but the results are often conflicting. Our hypothesis is that users are not fully aware of the features of the location-aware scenario and this lack of knowledge affects the results. Hence, in this paper we present a different approach: the analysis is conducted on two different groups of users (digital natives and digital immigrants) and is divided into two steps: (i) understanding users’ knowledge of a location-aware scenario and (ii) investigating users’ opinion toward location-aware services after showing them an example of an effective location-aware service able to extract personal and sensitive information from contents publicly available in social media platforms. The analysis reveals that there is relation between users’ knowledge and users’ concerns toward privacy in location-aware services and also reveals that digital natives are more interested in the location-aware scenario than digital immigrants. The analysis also discloses that users’ concerns toward these services may be ameliorated if these services ask for users’ authorization and provide benefits to users. Other interesting findings allow us to draw guidelines that might be helpful in developing effective location-aware services.

TSentiment: On gamifying Twitter sentiment analysis

Abstract: Social media platforms contain interesting information that can be used to directly measure people' feelings and, thanks to the use of communication technologies, also to geographically locate these feelings. Unfortunately, the understanding is not as easy as one may think. Indeed, the large volume of data makes the manual approach impractical and the diversity of language combined with the brevity of the texts makes the automatic approach quite complicated. In this paper, we consider the gamification approach to sentimentally classify tweets and we propose TSentiment, a game with a purpose that uses human beings to classify the polarity of tweets (e.g., positive, negative, neutral) and their sentiment (e.g., joy, surprise, sadness, etc.). We created a dataset of more than 65,000 tweets, we developed a Web-based game and we asked students to play the game. Obtained results showed that the game approach was well accepted and thus it can be useful in scenarios where the identification of people' feelings may bring benefits to decision making processes.

On using cashtags to predict companies stock trends

Abstract: Different theories state that future market values strongly depend on psychological and financial factors: when investors feel positive moods they invest and the value of the stock market increases; conversely, when they feel negative moods they do not invest and the value of the stock market decreases. Today, researchers are trying to exploit the data publicly available in social media and, in particular, different researches showed a connection between Twitter messages and the stock market index. In this paper, we do not focus on a generic stock market index, now we focus on the sole sentiment analysis. Instead, our goal is to investigate whether tweet messages can be used to predict the future trend (e.g., positive, negative or neutral) of the stocks of specific companies listed in the Dow Jones stock market. In particular, we focus on companies belonging to three different economic sectors (technology, service and health-care) and we consider the trend of 5 different metrics for each stock (e.g., highest, lowest, opening price, etc.) and the trend of 13 different variables of the tweets (e.g., volume, sentiment, tweets with links, etc.). Through an evaluation that employed more than 800,000 tweets, we show that some of the proposed ad-hoc prediction methods well predict (i.e., up to 82% of success) the next day trend of the stock values of specific companies.

On helping broadcasters to promote TV-shows through hashtags

Abstract: Television is no longer the king of the living room: 86% of people watch TV with a second screen in the nearby and more than 30% of the attention time is given to the second device to perform social activities. Therefore, the television industry is facing a new challenge: find a way to re-catch viewers’ attention. A recent and popular approach considers the use of official hashtags to implement a cross-media strategy that will connect different contents across multiple media. Since the simple proposition of official hashtags is not sufficient to guarantee the success of a cross media strategy, in this paper, we analyze how official hashtags are used and we propose a model to write tweets effectively. The model is based on the analysis of more than 250,000 tweets that talk about TV-shows. In particular, (i) we analyze the availability and visibility of official hashtags, (ii) we study the tweets characteristics written by the most retweeted authors, and (iii) we build a network of hashtags in order to understand how users use official hashtags. The obtained results allowed us to define guidelines to help broadcasters in the promotion of TV-shows and in the engagement of viewers.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

How to… home page

Abstract: Home PURPOSE OF THE CENTER: To develop the center to address state-of-the-art research, create innovating educational programs, and support technology transfers using commercially viable results to assist the Army Research Laboratory to develop the next generation Future Combat System in the telecommunications sector that assures prevention of perceived threats, and Non Line of Sight/Beyond Line of Sight lethal support.

Intelligent distributed surveillance systems: a review

Abstract: This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions.