1,721,011 research outputs found
An automatic mechanism to provide privacy awareness and control over unwittingly dissemination of online private information
No full textGiven the increasing popularity of social media and other Internet-related technologies, individuals spend a lot of time across different online activities, such as doing Google searches or credit card purchases, enjoying social networks interactions, performing job finding or travel plannings activities. Unfortunately, very often, individuals unwittingly disseminate a huge amount of personal and sensitive information that fundamentally represents an essential part of their private life. A large part of this information is embedded into text messages typed during online activities. Therefore, there is an increasing need for mechanisms to assist individuals during such activities, raising their awareness about potential violation of privacy at the time of disclosure; however, it is also essential to give them full control on whether and how to manage their data, thereby empowering them to make heedful decisions. The awareness can be realized through simple alert/highlight mechanisms, while the full control can be ensured by allowing users to make the final choice, that is, ignore warnings, or conversely accept them and thus (a) think twice before disseminating data (to avoid future regrets), or (b) anyway send data, but only after their anonymization. In this paper, we propose a novel approach based on machine learning and sentence embedding techniques with the primary goal of providing privacy awareness to users and, as a consequence, full control over their data during online activities. Our approach relies on the definition of four modules: (i) the Keyword module, which identifies personal and sensitive data in a text (from the syntactic point of view); (ii) the Topic module, which is devoted to understand the topic treated in text messages; (iii) the Sensitiveness module, which identifies sensitive information (from the semantic point of view) into text messages; lastly, (iv) the Personalization module, which goal is to learn the personal attitude of a user towards his/her own privacy (through opportune feedback) and therefore report the correct alert messages. We provided an implementation of such an approach, named Knoxly, as a prototype of a Google Chrome extension. The tool has undergone a preliminary experimental study to assess its effectiveness in terms of sensitive information identification accuracy, and its efficiency in terms of impact on user experience
Characterizing the behavioral evolution of twitter users and the truth behind the 90-9-1 rule
No full textOnline Social Networks (OSNs) represent a fertile field to collect real user data and to explore OSNs user behavior. Recently, two topics are drawing the attention of researchers: the evolution of online social roles and the question of participation inequality. In this work, we bring these two fields together to study and characterize the behavioral evolution of OSNs users according to the quantity and the typology of their social interactions. We found that online participation on the microblogging platform can be categorized into four different activity levels. Furthermore, we empirically verified that the 90-9-1 rule of thumb about participation inequality is not an accurate representation of reality. Findings from our analysis reveal that lurkers are less than expected: they are not 9 out of 10 as suggested by Nielsen, but 3 out of 4. This represents a significant result that can give new insights on how users relate with social media and how their use is evolving towards a more active interaction with the new generation of consumers
Techno-regulation and intelligent safeguards: Analysis of touch gestures for online child protection
No full textThe growth of Internet and the pervasiveness of ICT have led to a radical change in social relationships. One of the drawbacks of this change is the exposure of individuals to threats during online activities. In this context, the techno-regulation paradigm is inspiring new ways to safeguard legally interests by means of tools allowing to hamper breaches of law. In this paper, we focus on the exposure of individuals to specific online threats when interacting with smartphones. We propose a novel techno-regulatory approach exploiting machine learning techniques to provide safeguards against threats online. Specifically, we study a set of touch-based gestures to distinguish between underages or adults who is accessing a smartphone, and so to guarantee protection. To evaluate the proposed approach’s effectiveness, we developed an Android app to build a dataset consisting of more than 9000 touch-gestures from 147 participants. We experimented both single-view and multi-view learning techniques to find the best combination of touch-gestures able of distinguishing between adults and underages. Results show that the multi-view learning combining scrolls, swipes, and pinch-to-zoom gestures, achieves the best ROC AUC (0.92) and accuracy (88%) scores
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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