Fabio Celli

Social network data and practices: the case of friendfeed

Due to their large worldwide adoption, Social Network Sites (SNSs) have been widely used in many global events as an important source to spread news and information. While the searchability and persistence of this information make it ideal for sociological research, a quantitative approach is still challenging because of the size and complexity of the data. In this paper we provide a first analysis of Friendfeed, a well-known and feature-rich SNS.

Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures

In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook. We recruited volunteers among Facebook users and collected a dataset of profile pictures, labeled with gold standard self-assessed personality and interaction style labels. Then, we exploited a bag-of-visual-words technique to extract features from pictures. Finally, different machine learning approaches were used to test the effectiveness of these features in predicting personality and interaction style traits. Our good results show that this task is very promising, because profile pictures convey a lot of information about a user and are directly connected to impression formation and identity management.

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?

The Workshop on Computational Personality Recognition 2014

The Workshop on Computational Personality Recognition aims to define the state-of-the-art in the field and to provide tools for future standard evaluations in personality recognition tasks. In the WCPR14 we released two different datasets: one of Youtube Vlogs and one of Mobile Phone interactions. We structured the workshop in two tracks: an open shared task, where participants can do any kind of experiment, and a competition. We also distinguished two tasks: A) personality recognition from multimedia data, and B) personality recognition from text only. In this paper we discuss the results of the workshop.

Automatic identification of semantic relations in Italian complex nominals

This paper addresses the problem of the identification of the semantic relations in Italian complex nominals (CNs) of the type N+P+N. We exploit the fact that the semantic relation, which is underspecified in most cases, is partially made explicit by the preposition. We develop an annotation framework around five different semantic relations, which we use to create a corpus of 1700 Italian CNs, obtaining an inter-annotator agreement of K=.695. Exploiting this data, for each preposition p we train a classifier to assign one of the five semantic relations to any CN of the type N+p+N, by using both string and supersense features. To obtain supersenses, we experiment with a sequential tagger as well as a plain lookup in MultiWordNet, and find that using information obtained from the former yields better results.

Long Chains or Stable Communities? The Role of Emotional Stability in Twitter Conversations

In this article, we address the issue of how emotional stability affects social relationships in Twitter. In particular, we focus our study on users’ communicative interactions, identified by the symbol “@.” We collected a corpus of about 200,000 Twitter posts, and we annotated it with our personality recognition system. This system exploits linguistic features, such as punctuation and emoticons, and statistical features, such as follower count and retweeted posts. We tested the system on a data set annotated with personality models produced by human subjects and against a software for the analysis of Twitter data. Social network analysis shows that, whereas secure users have more mutual connections, neurotic users post more than secure ones and have the tendency to build longer chains of interacting users. Clustering coefficient analysis reveals that, whereas secure users tend to build stronger networks, neurotic users have difficulty in belonging to a stable community; hence, they seek for new contacts in online social networks.

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding peoples behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism.

Computational Approaches to the Analysis of Human Creativity

In this paper we address the issue of creativity and style computation from a natural language processing perspective. We introduce a computational framework for creativity analysis with two approaches, one agnostic, based on clustering, and one knowlegde-based, that exploits supervised learning and feature selection. While the agnostic approach can reveal the uniqueness of authors in a meaningful context, the knowledge-based approach can be exploited to extract the culturally relevant features of works and to predict social acceptance. In both the approaches, it is required a great effort to define symbols to represent meaningful cues in creativity and style.

Profilio: Psychometric Profiling to Boost Social Media Advertising

Profilio Company is a startup in its early business development stage that has developed a profiling solution for the field of paid social media advertising. In particular, the solution is designed for the enrichment of Customer Relationship Managements data and for segmentation of customer audiences. Three different Proof of Concepts with different clients have showed that the solution reduces the costs of paid social media advertising in different settings and with different advertising targets, especially starting from large audiences. In this paper we report the details about Profilios business idea, the development of Profilios technologies and the results of the Proof of Concepts.

Reports on the 2013 Workshop Program of the Seventh International AAAI Conference on Weblogs and Social Media

The Workshop Program of the Program of the Seventh International AAAI Conference on Weblogs and Social Media was held July 11, 2013, in Cambridge, Massachusetts. The program included four workshops, Computational Personality Recognition (Shared Task) (WS-13-01), Social Computing for Workforce 2.0 (WS-13-02), Social Media Visualization 2 (WS-13-03), and When the City Meets the Citizen (WS-13-04). This report summarizes the activities of the four workshops.