The impact of ocean acidification on the skeletal ossification in herring larvae (Clupea harengus, L.)

In the era of pervasive mobile computing, human encounters can be leveraged to enable new forms of social interactions mediated by the personal devices of individuals. In this framework, emerging needs, such as content dissemination, social discovery and question&answering, advocate the raising of novel communication paradigms where the binding content-recipients is not provided by the sender (in the classical IP addressing style), but directly executed by specific recipients with interest on it. This allows tagged contents to be freely advertised on the network according to a content-driven approach; human encounters drive the information towards potential recipients that extract it from the stream when content type and personal interest match. This very active research area has recently produced a few preliminary solutions to this networking problem; they inherently confine message delivery inside a specific location and/or community. This covers only a part of users needs, as emerging from everyday life experience and recent studies in human sciences. This paper proposes a novel communication protocol, named InterestCast, or I Cast, solving the problem for a wide range of social scenarios and applying to a delay tolerant ad hoc network whose nodes are the personal device of moving individuals, possibly interacting with fixed road-side devices. The protocol is able to chase users interests decoupling content tags from locations and social communities. The main advantages the proposal achieves are: it ensures remarkable performance results; it is simple and, thus, it is feasible and keeps computational and networking costs low; it preserves users privacy.erant ad hoc network whose nodes are the personal device of moving individuals, possibly interacting with fixed road-side devices. The protocol is able to chase users interests decoupling content tags from locations and social communities. The main advantages the proposal achieves are: it ensures remarkable performance results; it is simple and, thus, it is feasible and keeps computational and networking costs low; it preserves users privacy

Predicting encounter and colocation events

Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people\u2019s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user\u2019s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard na\uefve Bayesian and some of the state of the art predictors

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

On the properties of human mobility

The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest - PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research

Fine-Grained Tracking of Human Mobility in Dense Scenarios

This paper envisions an urban scenario where people carry radio devices that can be dynamically networked, by exploiting human contact opportunities, to create unplanned, improvised and localized wireless connectivity, which has been recently called pocket switched networks (PSN).The paper focuses on the radio device (pocket mobility trace recorder, or PMTR) we have on purposely designed and developed to improve this understanding by enabling the gathering of rich and detailed mobility data sets from experiments in real mobility settings. The main contribution of the paper is twofold: we firstly describe the architecture of the radio devices and, secondly, we provide some evidence of the impact short contacts have on forwarding in dense settings

Follow the “mastodon”: Structure and evolution of a decentralized online social network

In this paper we present a dataset containing both the network of the \u201cfollow\u201d relationships and its growth in terms of new connections and users, all which we obtained by mining the decentralized online social network named Mastodon. The dataset is combined with usage statistics and meta-data (geographical location and allowed topics) about the servers comprising the platform-s architecture. These server are called instances. The paper also analyzes the overall structure of the Mastodon social network, focusing on its diversity w.r.t. other commercial microblogging platforms such as Twitter. Finally, we investigate how the instance-like paradigm influences the connections among the users. The newest and fastest-growing microblogging platform, Mastodon is set to become a valid alternative to established platforms like Twitter. The interest in Mastodon is mainly motivated as follows: a) the platform adopts an advertisement and recommendation-free business model; b) the decentralized architecture makes it possible to shift the control over user contents and data from the platform to the users; c) it adopts a community-like paradigm from both user and architecture viewpoints. In fact, Mastodon is composed of interconnected communities, placed on different servers; in addition, each single instance, with specific topics and languages, is independently owned and moderated. The released dataset paves the way to a number of research activities, which range from classic social network analysis to the modeling of social network dynamics and platform adoption in the early stage of the service. This data would also enable community detection validation since each instance hinges on specific topics and, lastly, the study of the interplay between the physical architecture of the platform and the social network it supports

Calling, texting, and moving : multidimensional interactions of mobile phone users

The communication networks obtained by using mobile phone datasets have drawn increasing attention in recent years. Studies have led to important advances in understanding the behavior of mobile users although they have just considered text message (short message service (SMS)), call data, and spatial proximity, separately. However, there is a growing awareness that human sociality is expressed simultaneously on multiple layers, each corresponding to a specific way an individual has to communicate. In fact, besides the common real life encounters, a mobile phone user has at least two further communication media to exploit, SMSs and voice calls. This is advocating a multidimensional approach if we are seeking a compound description of the human mobile social behavior. In this context, we perform the first study of the multiplex mobile network, gathered from the records of both call and text message activities, along with relevant geographical information, of millions of users of a large mobile phone operator over a period of 12 weeks. By computing a set of complex network metrics, at different scales, onto the three single layers given by calls, SMSs and spatial proximity, and their extensions onto a three-level network, we provide a comprehensive study of the global multi-layered network which arises from both the overall on-the-phone communications performed by mobile users and their spatial propinquity

Urban groups : behavior and dynamics of social groups in urban space

The tendency of people to form socially cohesive groups that get together in urban spaces is a fundamental process that drives the formation of the social structure of cities. However, the challenge of collecting and mining large-scale data able to unveil both the social and the mobility patterns of people has left many questions about urban social groups largely unresolved. We leverage an anonymized mobile phone dataset, based on Call Detail Records (CDRs), which integrates the usual voice call data with text message and Internet activity information of one million mobile subscribers in the metropolitan area of Milan to investigate how the members of social groups interact and meet onto the urban space. We unveil the nature of these groups through an extensive analysis, along with proposing a methodology for their identification. The findings of this study concern the social group behavior, their structure (size and membership) and their root in the territory (locations and visit patterns). Specifically, the footprint of urban groups is made up by a few visited locations only; which are regularly visited by the groups. Moreover, the analysis of the interaction patterns shows that urban groups need to combine frequent on-phone interactions with gatherings in such locations. Finally, we investigate how their preferences impact the city of Milan telling us which areas encourage group get-togethers best

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