100,793 research outputs found
Social Media and Fake News Detection using Adversarial Collaboration
The diffusion of fake information on social media networks obscures public perception of events, news, and relevant content. Intentional misleading news may promote negative online experiences and influence societal behavioral changes such as increased anxiety, loneliness, and inadequacy. Adversarial attacks target creating misinformation in online information systems. This behavior can be viewed as an instrument to manipulate the online social media networks for cultural, social, economic, and political gains. A method to test a deep learning model- long short-term memory (LSTM) using adversarial examples generated from a transformer model has been presented. The paper attempts to examine features in machine learning algorithms that propagate fake news. Another goal is to evaluate and compare the usefulness of generative adversarial networks with long-term short-term recurrent neural network algorithms in identifying fake news. A closer look at the mechanisms of implementing adversarial attacks in social media systems helps build robust intelligent systems that can withstand future vulnerabilities
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
Locating privileged spreaders on an Online Social Network
Social media have provided plentiful evidence of their capacity for
information diffusion. Fads and rumors, but also social unrest and riots travel
fast and affect large fractions of the population participating in online
social networks (OSNs). This has spurred much research regarding the mechanisms
that underlie social contagion, and also who (if any) can unleash system-wide
information dissemination. Access to real data, both regarding topology --the
network of friendships-- and dynamics --the actual way in which OSNs users
interact--, is crucial to decipher how the former facilitates the latter's
success, understood as efficiency in information spreading. With the
quantitative analysis that stems from complex network theory, we discuss who
(and why) has privileged spreading capabilities when it comes to information
diffusion. This is done considering the evolution of an episode of political
protest which took place in Spain, spanning one month in 2011.Comment: 7 pages, 4 figure
Analysis of the Structure of Social Networks for Information Diffusion
The vast proliferation of Online Social Networks (OSN) is generating many new ways to interact and create social relationships with others.
In OSN, information spreads among users following existing social relationships. This spread is influenced by the local properties and structures of the social relationships at individual level. Being able to understand these properties can be fundamental for the design of new communication systems able to predict the creation and sharing of content based on social properties of the users.
While substantial results have been obtained in anthropology literature describing the properties of human social networks, a clear understanding of the properties of social networks built using OSN is still to be achieved.
In this thesis, the structure of Ego networks formed online is compared with the properties of offline social relationships showing interesting similarities. These properties are exploited to provide a meaningful way to study the mechanisms controlling the formation of information diffusion chains in social networks (typically referred to as information cascades). Trough the analysis of synthetically generated diffusion cascades executed in a large Facebook communication datasets, is showed that the knowledge of tie strength of the social links is fundamental to infer which nodes will give rise to large information cascades and which links will be more used in the information diffusion process. We analysed the trade off between information spread and trustworthiness of information. Specifically, we have investigated the spread of information when only links of a certain trust value are used. Assuming, based on results from sociology, that trust can be quantised, we show that too strict limits on the minimum trust between users limit significantly information spread. In the thesis we investigate the effect of different strategies to significantly increase spread of information by minimally relaxing constraints on the minimum allowed trust level
Social networks and communication behaviour underlying smart home adoption in the UK
Consumer-facing digital innovations with the potential to reduce carbon emissions often exist in small market niches and their impact has been limited thus far. Using the established Diffusion of Innovations theory which considers interpersonal communication amongst social networks to be a vital mechanism for exchanging information, we conducted an online survey in the UK to investigate the social networks and communication behaviours of adopters and non-adopters of three different energy saving smart home technologies. Applying social network analysis and statistically testing hypotheses, our results reveal the potential social barriers to the diffusion of information, with social network structure and characteristics creating obstacles. This research provides necessary insights into real early adopters, confirms the importance of focussing research on the often-neglected social elements of diffusion theory and helps identify marketing strategies and policy actions using social mechanisms to accelerate a low carbon transition
The Dynamics of Protest Recruitment through an Online Network
The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action
- …