Mastodon Content Warnings: Inappropriate Contents in a Microblogging Platform

Our social communications and the expression of our beliefs and thoughts are becoming increasingly mediated and diffused by online social media. Beyond countless other advantages, this democratization and freedom of expression is also entailing the transfer of unpleasant offline behaviors to the online life, such as cyberbullying, sexting, hate speech and, in general, any behavior not suitable for the online community people belong to. To mitigate or even remove these threats from their platforms, most of the social media providers are implementing solutions for the automatic detection and filtering of such inappropriate contents. However, the data they use to train their tools are not publicly available. In this context, we release a dataset gathered from Mastodon, a distribute online social network which is formed by communities that impose the rules of publication, and which allows its users to mark their posts inappropriate if they perceived them not suitable for the community they belong to. The dataset consists of all the posts with public visibility published by users hosted on servers which support the English language. These data have been collected by implementing an ad-hoc tool for downloading the public timelines of the servers, namely instances, that form the Mastodon platform, along with the meta-data associated to them. The overall corpus contains over 5 million posts, spanning the entire life of Mastodon. We associate to each post a label indicating whether or not its content is inappropriate, as perceived by the user who wrote it. Moreover, we also provide the full description of each instance. Finally, we present some basic statistics about the production of inappropriate posts and the characteristics of their associated textual content

Designing rigid carbon foams

We use ab initio density functional calculations to study the stability, elastic properties and electronic structure of sp2 carbon minimal surfaces with negative Gaussian curvature, called schwarzites. We focus on two systems with cubic unit cells containing 152 and 200 carbon atoms, which are metallic and very rigid. The porous schwarzite structure allows for efficient and reversible doping by electron donors and acceptors, making it a promising candidate for the next generation of alkali ion batteries. We identify schwarzite structures that act as arrays of interconnected quantum spin dots or become magnetic when doped. We introduce two interpenetrating schwarzite structures that may find their use as the ultimate super-capacitor.Comment: 6 pages, 5 figure

Walls-in-one : usage and temporal patterns in a social media aggregator

The continual launches of new online social media that meet the most varied people\u2019s needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people\u2019s identity and behavior are often partial, incomplete and scattered in different \u201cplaces\u201d. To overcome this identity fragmentation and to give an all-around picture of people\u2019s online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator. The analysis of social media usage shows that Alternion data reflect the novel trend of today\u2019s users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users\u2019 degree in five different social networks, we performed a rank correlation analysis on users\u2019 degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites

Memory and burstiness in dynamic networks

A discrete-time random process is described, which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x, is modified to include a memory effect where the event probability is increased proportionally to the number of events that occurred within a given amount of time preceding t. For small values of x the interevent time distribution follows a power law with exponent −2−x. We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, \rho(x), from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data, such as online social communications and fMRI scans

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded"social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis. Copyright

Managerial Hubris, Trade-Associations, and Regulatory Knowledge in Micro-Firms

To avoid breaking the law for regulatory non-compliance, it is essential that micro-firm owner-managers are aware of deficiencies in their knowledge, so that they can seek improvement and avoid over-confidence (i.e. hubris) in their knowledge levels. Using newly collected survey data from micro-firms in the English accommodation sector and multivariate techniques, the authors explore the possibility of hubris by making a novel distinction between the Perceived-Knowledge and Actual-Knowledge of regulation held by micro-firm owner-managers. Both Perceived-Knowledge (from self-assessment) and Actual-Knowledge (from a simple test) over four core areas of regulation are found to be different, generally poor and suggestive of hubris. The relationship between these knowledge levels is further explored by considering the role of trade association membership (since they support members) and attitude (since it effects learning). Attitude is found to be positively associated with both forms of knowledge, while trade association memberships are also found to be associated with enhanced Perceived-Knowledge, but not Actual-Knowledge. In light of the results, the authors suggest several priority areas for improving Actual-Knowledge and self-assessment skills, and areas for future research.</p