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29: Hybrid crowdsensing in earthquake emergency relief. Analysis of cooperativeness in replies to our tweets.
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The recent proliferation of handheld devices that are equipped with a large number of sensors and communication capabilities, as well as the ubiquitous presence of communication facilities and infrastructures, and the mass diffusion and availability of social networking applications, has created a socio-technical convergence capable of sparking a r...
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... Micro-blogging platforms, such as Twitter, and platforms for sharing photographs and videos, such as Flickr and Instagram, allow the users for the annotation of their content with geographic coordinates. The high popularity of these services in conjunction with the widespread proliferation of devices capable of providing location information has led to great and constantly increasing volumes of location-and time-referenced data produced by myriads of users [38]. By analyzing these data, it is possible to extract interesting new information about various places and events as well as about people's interests, mobility behaviors, and lifestyles. ...
... As more exchanges of sensor data are performed, the nodes turn to green, indicating a maximal accurate estimation of the aggregation functions. DIAS eliminates the communication cost as accuracy increases and devices aggregate acquire the available sensor data in the network [94] Algorithmic tools addressing critical challenges in urban data science: (i) how to model information extracted from location-based social networks, (ii) TOSCA, RAMA-location detection, (iii) DITRAS-simulation of realistic mobility, (iv) MyWay-individual movement prediction [10,34,43,51,52,79,100,107,112] Visual analytics for urban data Visual analytics for geolocated social media data: photograph sharing and micro-blogging platforms [3][4][5]5,6,38,62] Shaping urban landscape Use of big data analytics for (i) recommendation to tourists (TRIPBUILDER), (ii) improving shared mobility, (iii) studying the link between human mobility, socioeconomic development, urban sustainability, and net negative cities [17,[21][22][23]27,39,44,45,60,70,101,114] SoBigData software suites Fully fledged platforms: (i) the M-Atlas tool for mining spatiotemporal data, (ii) EPOS for self-regulating sharing economies [57,95,96] Privacy-aware data gathering and protection New deal on data: (i) managing mobility data (ii) anonymization, (iii) PRUDEnce framework, (iv) DIAS [11,18,37,39,47,59,86,94] vacy, so this research challenge is as much about learning with complex and heterogeneous data as it is about privacypreserving data mining. ...
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.
... These unprecedented sharing and sensing opportunities have enabled situations where individuals not only play the role of sensor operators, but also act as data sources themselves, thus implementing the so-called Human-as-a-Sensor paradigm. This spontaneous behavior has driven a new thriving -yet challenging -research field, called social sensing, investigating how human-sourced data can be gathered and used to gain situational awareness in a number of socially relevant domains [10]. Depending on their awareness and their involvement in the system, ''human sensors'' are faced with either opportunistic sensing, where users spontaneously collect and share data that is transparently intercepted by a situationaware system -or with participatory sensing, where users consciously meet an application request out of their own will [11]. ...
People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.
... However, the democratizing effect of OSNs does not come without costs [6]. In 2016, "post-truth" was selected by the Oxford dictionary as the word of the year, and in 2017 "fake news" was selected for the same purpose by Collins dictionary. ...
... It would also be profitable to be able to evaluate detectors against possible evolved versions of current bots, by applying the adversarial approach previously described. In order to reach this ambitious goal, we must first create reference datasets that comprise several different kinds of bots, thus significantly adding to the sparse resources existing as of today 6 . Then, as already anticipated, we should also devise additional ways for creating a broad array of diverse adversarial examples. ...
Recently, studies on the characterization and detection of social bots were published at an impressive rate. By looking back at over ten years of research and experimentation on social bots detection, in this paper we aim at understanding past, present, and future research trends in this crucial field. In doing so, we discuss about one of the nastiest features of social bots – that is, their evolutionary nature. Then, we highlight the switch from supervised bot detection techniques – focusing on feature engineering and on the analysis of one account at a time – to unsupervised ones, where the focus is on proposing new detection algorithms and on the analysis of groups of accounts that behave in a coordinated and synchronized fashion. These unsupervised, group-analyses techniques currently represent the state-of-the-art in social bot detection. Going forward, we analyze the latest research trend in social bot detection in order to highlight a promising new development of this crucial field.
... These unprecedented sharing and sensing opportunities have enabled situations where individuals not only play the role of sensor operators, but also act as data sources themselves, thus implementing the so-called Human-as-a-Sensor paradigm. This spontaneous behavior has driven a new thriving -yet challenging -research field, called social sensing, investigating how human-sourced data can be gathered and used to gain situational awareness in a number of socially relevant domains [10]. Depending on their awareness and their involvement in the system, "human sensors" are faced with either opportunistic sensing, where users spontaneously collect and share data that is transparently intercepted by a situation-aware system -or with participatory sensing, where users consciously meet an application request out of their own will [11]. ...
People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.
... However, the democratizing effect of OSNs does not come without costs [6]. In 2016, "post-truth" was selected by the Oxford dictionary as the word of the year, and in 2017 "fake news" was selected for the same purpose by Collins dictionary. ...
Recently, studies on the characterization and detection of social bots were published at an impressive rate. By looking back at over ten years of research and experimentation on social bots detection, in this paper we aim at understanding past, present, and future research trends in this crucial field. In doing so, we discuss about one of the nastiest features of social bots-that is, their evolutionary nature. Then, we highlight the switch from supervised bot detection techniques - focusing on feature engineering and on the analysis of one account at a time - to unsupervised ones, where the focus is on proposing new detection algorithms and on the analysis of groups of accounts that behave in a coordinated and synchronized fashion. These unsupervised, group-analyses techniques currently represent the state-of-the-art in social bot detection. Going forward, we analyze the latest research trend in social bot detection in order to highlight a promising new development of this crucial field.
... In addition to fake information and the spreading of rumors, other types of deception (e.g., spambots and fake followers) in social media also compromise the effectiveness and efficiency of information dissemination and acquisition. Cresci (2018) presented a series of studies on accurate and efficient detection malicious accounts and future work may explore this application in a disaster scenario. ...
... This section reviews the approaches that assess the damage caused by disasters, which is critical for providing understandable actionable information to the public and disaster management personnel (Cresci, 2018). Therefore, approaches reviewed in this subsection unveils the societal considerations and social impacts of disaster victims, which can help achieve Function 3 of the Vision. ...
... It is not surprising that OSNs have also been exploited for maliciously influencing the public opinion [1,2]. One common way to achieve this goal is to employ large groups of automated (bot) accounts (henceforth spambots) that repeatedly spam polarized content. ...
Since decades, genetic algorithms have been used as an effective heuristic to solve optimization problems. However, in order to be applied, genetic algorithms may require a string-based genetic encoding of information, which severely limited their applicability when dealing with online accounts. Remarkably, a behavioral modeling technique inspired by biological DNA has been recently proposed – and successfully applied – for monitoring and detecting spambots in Online Social Networks. In this so-called digital DNA representation, the behavioral lifetime of an account is encoded as a sequence of characters, namely a digital DNA sequence. In a previous work, the authors proposed to create synthetic digital DNA sequences that resemble the characteristics of the digital DNA sequences of real accounts. The combination of (i) the capability to model the accounts’ behaviors as digital DNA sequences, (ii) the possibility to create synthetic digital DNA sequences, and (iii) the evolutionary simulations allowed by genetic algorithms, open up the unprecedented opportunity to study – and even anticipate – the evolutionary patterns of modern social spambots. In this paper, we experiment with a novel ad-hoc genetic algorithm that allows to obtain behaviorally evolved spambots. By varying the different parameters of the genetic algorithm, we are able to evaluate the capability of the evolved spambots to escape a state-of-art behavior-based detection technique. Notably, despite such detection technique achieved excellent performances in the recent past, a number of our spambot evolutions manage to escape detection. Our analysis, if carried out at large-scale, would allow to proactively identify possible spambot evolutions capable of evading current detection techniques.
Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.
