Paolo Fornacciari

Dynamics of Emotions and Relations in a Facebook Group of Patients with Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is an orphan, underdiagnosed and painful disease of the skin that has a considerable negative impact on quality of life and on emotional well-being. As reported by the italian HS patients’ association (Inversa Onlus), this condition brings patients to develop an emotional closure with the consequence that they often don’t talk about their condition with anybody. In this paper we discuss some results obtained by applying automatic emotion detection and social network analysis techniques on the Facebook group of the Inversa Onlus association. In particular, we analyze the patients’ emotional states, as expressed by the post published from 2010 to 2016, and how these emotions are influenced by friendships in the group, during the years.

Social Relevance Index for Studying Communities in a Facebook Group of Patients

Identifying Relevant Sets, i.e., variable subsets that exhibit a coordinated behavior, in complex systems is a very relevant research topic. Systems that exhibit complex dynamics are, for example, social networks, which are characterized by complex and dynamic relationships among users. A challenging topic within this context regards the identification of communities or subsets of users, both within the whole network and within specific groups. We applied the Relevance Index method, which has been shown to be effective in many situations, to the study of communities of users in the Facebook group of the Italian association of patients affected by Hidradenitis Suppurativa. Since the need for computing the Relevance Index for each possible variable subset of users makes the exhaustive computation unfeasible, we resorted to the help of an efficient niching evolutionary metaheuristic, hybridized with local searches. The communities detected through the aforementioned method have been studied to search similarities in terms of number of posts, sentiments, number of contacts, roles, behaviors, etc. The results demonstrate that it is possible to detect such subsets of users in the particular Facebook group we analyzed.

Automatic Creation of a Large and Polished Training Set for Sentiment Analysis on Twitter

Within the field of sentiment analysis and emotion detection applied to tweets, one of the main problems related to the construction of an automatic classifier is the lack of suitable training sets. Considering the tediousness of manually annotating a training set, and the noise present in data collected directly from the social web, in this paper we propose an iterative learning approach, which combines distant supervision with dataset pruning techinque. In particular, following the “eat your own dogfood” idea, we have applied a classifier, trained on raw data obtained from different Twitter channels, to the same original dataset, for removing the most doubious instances automatically. This kind of approach has been used to obtain a more polished training set for emotion classification, based on Parrot’s model of six basic emotions. On the basis of the achieved results, we argue that the automatic filtering of training sets can make the application of the distant supervision approach more effective in many use cases.

Real Marks Analysis for Predicting Students’ Performance

In the last few years the amount of electronic data in high schools has grown tremendously, also as a consequence of the introduction of electronic logbooks, where teachers store data about their students’ activities: school attendance, marks obtained in individual test trials and the typology of these tests. However, all this data is often spread across multiple providers and it is not always easily available for research purposes. Our research project, named ELDM (Electronic Logbook Data Mining), focuses exactly on this information. In particular, we have developed a web-based system which is freely usable by school stakeholders; it allows them to (i) easily share school data with the ELDM project, and (ii) check the students’ very different learning levels. On the basis of data collected from adhering schools, we have applied data mining techniques to analyze all the students’ behaviours and results. Our findings show that: (i) it is possible to anticipate the outcome prediction in the first school months; and (ii) by focusing only on a small number of subjects, it is also feasible to detect serious didactic situations for students very early. This way, tutorship activities and other kinds of interventions can be programmed earlier and with greater effectiveness.

Actors Based Agent Modelling and Simulation

Agent-based modeling and simulation are some powerful techniques that are widely used with success for analyzing complex and emergent phenomena in many research and application areas. Many different reasons are behind the success of such techniques, among which an important mention goes to the availability of a great variety of software tools, that ease the development of models, as well as the execution of simulations and the analysis of results. However, the agent models provided by such tools do not offer the features of the computational agents found in multi-agent systems or distributed artificial intelligence techniques. Therefore, it is difficult to use such tools to model complex systems defined by autonomous, proactive and social entities. This paper presents an actor software library, called ActoDeS, for the development of concurrent and distributed systems, and shows how it can be a suitable mean for building flexible and scalable ABMS applications.

A combined approach for the analysis of support groups on Facebook - the case of patients of hidradenitis suppurativa

Hidradenitis Suppurativa (HS), also known as Acne Inversa, is a chronic, underdiagnosed, often debilitating and painful disease that affects the folds of the skin. It has a considerable negative impact on the quality of life and on the emotional well-being. In this paper we discuss some results obtained by applying automatic Emotion Detection and Social Network Analysis techniques on the Facebook group of the Italian patients’ association (Inversa Onlus). In particular, we analyze the patients’ emotional states, as expressed by the posts and comments published from 2009 to 2017, and how these emotions are influenced by different social network factors, such as interactions and friendships in the group, during the observed years.

A holistic system for troll detection on Twitter

Abstract Various techniques based on artificial intelligence have been proposed for the automatic detection of online anti-social behaviors, both in existing systems and in the scientific literature. In this article, we describe TrollPacifier, a holistic system for troll detection, which analyses many different features of trolls and legitimate users on the popular Twitter platform. In this system, the most known and promising approaches and research lines are applied, along with original new ideas, in a form that fits such a large public platform. In particular, we have identified six groups of features, based respectively on the analysis of writing style, sentiment, behaviors, social interactions, linked media, and publication time. As its main scientific contributions, this work provides: (i) an up-to-date analysis of the state of the art for the problem of troll detection; (ii) the systematic collection and grouping of features, on Twitter; (iii) the description of a working holistic system for troll detection, with a very high accuracy (95.5%); and (iv) a comparison among the different features, with a machine learning approach. Our results demonstrate that automatic classification can be useful in the whole process of identification and management of online anti-social behaviors. However, a multi-faceted approach is required, in order to obtain an adequate accuracy.