Abstract. This article uses empirical evidence on networks of voluntary organizations mobilizing on ethnic minority, environmental, and social exclusion issues in two British cities, to differentiate between social movement processes and other, cognate collective action dynamics. Social movement processes are identified as the building and reproducing of dense informal networks between a multiplicity of actors, sharing a collective identity, and engaged in social and/or political conflict. They are contrasted to coalitional processes, where alliances to achieve specific goals are not backed by significant identity links, and organizational processes, where collective action takes place mostly in reference to specific organizations rather than broader, looser networks.Among his innumerable contributions to the study of social movements, Charles Tilly has provided practitioners with what is probably the most popular definition of their object of analysis: "a sustained series of interactions between power-holders and persons successfully claiming to speak on behalf of a constituency lacking formal representation, in the course of which those persons make publicly visible demands for change in the distribution or exercise of power, and back those demands with public demonstrations of support."1 Recently, however, the reference to "social movements" has lost centrality in his analytical scheme. The "Dynamics of contention" program (henceforth "Doc") invokes a reorientation of the social movement research agenda toward the identification of mechanisms, which may be found to operate across highly different episodes and forms of contentious politics.2 Its advocates regard social movements as a particular form of political participation 3 or, interchangeably, as broad episodes of contention -along with democratization, nationalism, and revolution -from the analysis of which we can extract specific social mechanisms. 4 There are very good reasons not to treat "social movements" as a distinct set of phenomena, or to posit that there should be specific intellectual sub-fields devoted to their exclusive study. Indeed, the whole development of the field reflects recurrent cross-fertilization

Among the general population, students are especially sensitive to social media and smartphones because of their pervasiveness. Several studies have shown that there is a negative correlation between social media and academic performance since they can lead to behaviors that hurt students' careers, e.g., addictedness. However, these studies either focus on smartphones and social media addictedness or rely on surveys, which only provide approximate estimates. We propose to bridge this gap by i) parametrizing social media usage and academic performance, and ii) combining smartphones and time diaries to keep track of users' activities and their smartphone interaction. We apply our solution on the 72 students participating in the SmartUnitn project, which investigates students' time management and their academic performance. By analyzing the logs of social media apps on students' smartphones while they are studying and attending lessons, and comparing them to students' credits and grades, we can provide a quantitative and qualitative estimate of negative and positive correlations. Our results show the negative impact of social media usage, distinguishing different influence patterns of social media on academic activities and underline the need to take it into account and control the smartphone usage in academic settings.

No abstract

Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shopping and online behaviors. However, the existing researches have been often limited to a single or to the combination of few behavioral dimensions, and they have adopted the perspective of an outside observer who is unaware of the motivations behind the specific behaviors or activities of a given individual. The key assumption of this work is that human behavior is deliberated based on an individual’s own perception of the situation that s/he is in, and that therefore it should also be studied under the same perspective. Taking inspiration from works in ubiquitous and context-aware computing, we investigate the role played by four contextual dimensions (or modalities), namely time, location, activity being carried out, and social ties, on the predictability of individuals’ behaviors, using a month of collected mobile phone sensor readings and self-reported annotations about these contextual modalities from more than two hundred study participants. Our analysis shows that any target modality (e.g. location) becomes substantially more predictable when information about the other modalities (time, activity, social ties) is made available. Multi-modality turns out to be in some sense fundamental, as some values (e.g. specific activities like “shopping”) are nearly impossible to guess correctly unless the other modalities are known. Subjectivity also has a substantial impact on predictability. A location recognition experiment suggests that subjective location annotations convey more information about activity and social ties than objective information derived from GPS measurements. We conclude the paper by analyzing how the identified contextual modalities allow to compute the diversity of personal behavior, where we show that individuals are more easily identified by rarer, rather than frequent, context annotations. These results offer support in favor of developing innovative computational models of human behaviors enriched by a characterization of the context of a given behavior.