Mobile Communication Signatures of Unemployment

The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution < 2.7km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Neural computations underpinning the strategic management of influence in advice giving

Research on social influence has focused mainly on the target of influence (e.g., consumer and voter); thus, the cognitive and neurobiological underpinnings of the source of the influence (e.g., politicians and salesmen) remain unknown. Here, in a three-sided advice-giving game, two advisers competed to influence a client by modulating their own confidence in their advice about which lottery the client should choose. We report that advisers’ strategy depends on their level of influence on the client and their merit relative to one another. Moreover, blood-oxygenation-level-dependent (BOLD) signal in the temporo-parietal junction is modulated by adviser’s current level of influence on the client, and relative merit prediction error affects activity in medial-prefrontal cortex. Both types of social information modulate ventral striatum response. By demonstrating what happens in our mind and brain when we try to influence others, these results begin to explain the biological mechanisms that shape inter-individual differences in social conduct

Adaptive social networks promote the wisdom of crowds

Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network “edges” encode the computation); and 2) a local-adaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the “node” in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments

Adaptive social networks promote the wisdom of crowds

© 2020 National Academy of Sciences. All rights reserved. Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network "edges" encode the computation); and 2) a localadaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the "node" in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments

A hybrid approach for detecting spammers in online social networks

Evolving behaviours by spammers on online social networks continue to be a big challenge; this phenomenon has consistently received attention from researchers in terms of how it can be combated. On micro-blogging communities, such as Twitter, spammers intentionally change their behavioral patterns and message contents to avoid detection. Many existing approaches have been proposed but are limited due to the characterization of spammers' behaviour with unified features, without considering the fact that spammers behave differently, and this results in distinct patterns and features. In this study, we approach the challenge of spammer detection by utilizing the level of focused interest patterns of users. We propose quantity methods to measure the change in user's interest and determine whether the user has a focused-interest or a diverse-interest. Then we represent users by features based on the level of focused interest. We develop a framework by combining unsupervised and supervised learning to differentiate between spammers and legitimate users. The results of this experiment show that our proposed approach can effectively differentiate between spammers and legitimate users regarding the level of focused interest. To the best of our knowledge, our study is the first to provide a generic and efficient framework to represent user-focused interest level that can handle the problem of the evolving behaviour of spammers

Towards Designing a Knowledge Graph-Based Framework for Investigating and Preventing Crime on Online Social Networks

Online Social Networks (OSNs) have fundamentally and permanently altered the arena of digital and classical crime. Recently, law enforcement agencies (LEAs) have been using OSNs as a data source to collect Open Source Intelligence for fighting and preventing crime. However, most existing technological developments for LEAs to fight and prevent crime rely on conventional database technology, which poses problems. As social network usage is increasing rapidly, storing and querying data for information retrieval is critical because of the characteristics of social networks, such as unstructured nature, high volumes, velocity, and data interconnectivity. This paper presents a knowledge graph-based framework, an outline of a framework designed to support crime investigators solve and prevent crime, from data collection to inferring digital evidence admissible in court. The main component of the proposed framework is a hybrid ontology linked to a graph database, which provides LEAs with the possibility to process unstructured data and identify hidden patterns and relationships in the interconnected data of OSNs