311 research outputs found

    Publish, Share, Re-Tweet, and Repeat

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    New technologies allow users to communicate ideas to a broad audience easily and quickly, affecting the way ideas are interpreted and their credibility. Each and every social network user can simply click “share” or “retweet” and automatically republish an existing post and expose a new message to a wide audience. The dissemination of ideas can raise public awareness about important issues and bring about social, political, and economic change. Yet, digital sharing also provides vast opportunities to spread false rumors, defamation, and Fake News stories at the thoughtless click of a button. The spreading of falsehoods can severely harm the reputation of victims, erode democracy, and infringe on the public interest. Holding the original publisher accountable and collecting damages from him offers very limited redress since the harmful expression can continue to spread. How should the law respond to this phenomenon and who should be held accountable? Drawing on multidisciplinary social science scholarship from network theory and cognitive psychology, this Article describes how falsehoods spread on social networks, the different motivations to disseminate them, the gravity of the harm they can inflict, and the likelihood of correcting false information once it has been distributed in this setting. This Article will also describe the top-down influence of social media platform intermediaries, and how it enhances dissemination by exploiting users’ cognitive biases and creating social cues that encourage users to share information. Understanding how falsehoods spread is a first step towards providing a framework for meeting this challenge. The Article argues that it is high time to rethink intermediary duties and obligations regarding the dissemination of falsehoods. It examines a new perspective for mitigating the harm caused by the dissemination of falsehood. The Article advocates harnessing social network intermediaries to meet the challenge of dissemination from the stage of platform design. It proposes innovative solutions for mitigating careless, irresponsible sharing of false rumors. The first solution focuses on a platform’s accountability for influencing user decision-making processes. “Nudges” can discourage users from thoughtless sharing of falsehoods and promote accountability ex ante. The second solution focuses on allowing effective ex post facto removal of falsehoods, defamation, and fake news stories from all profiles and locations where they have spread. Shaping user choices and designing platforms is value laden, reflecting the platform’s particular set of preferences, and should not be taken for granted. Therefore, this Article proposes ways to incentivize intermediaries to adopt these solutions and mitigate the harm generated by the spreading of falsehoods. Finally, the Article addresses the limitations of the proposed solutions yet still concludes that they are more effective than current legal practices

    Combating Fake News on Social Media: A Framework, Review, and Future Opportunities

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    Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information is also widely spread, generally referred to as “fake news”. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from truth. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news to action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue; and as such, a collaborative effort from different fields is needed to effectively address this problem

    Freedom on the Net 2014 - Tightening the Net: Governments Expand Online Controls (Summary)

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    Internet freedom around the world has declined for the fourth consecutive year, with a growing number of countries introducing online censorship and monitoring practices that are simultaneously more aggressive and more sophisticated in their targeting of individual users. This booklet is a summary of findings for the 2014 edition of "Freedom on the Net.

    MANAGING INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS VIA STRUCTURAL ANALYSIS

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    Ph.DDOCTOR OF PHILOSOPH

    Fake news: public policy responses

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    An apparent proliferation of inaccurate and misleading news stories has led to calls for new policy interventions, from fact checking by social media companies to new laws imposing fines for posting or sharing fake news. This raises some difficult issues in media policy. Is this a new problem? Is so called ‘fake news’ distinct from longstanding problems with accuracy or objectivity in journalism? Is the controversy rather a response to the scale of current political changes, and their impact on various interested parties? Are there fundamental changes going on in our Western media systems which undermine traditional journalistic crafts of fact checking and verification, and incentivise more emotionally resonant content, at the expense of quality, reliable journalism

    A Study on the Improvement of Data Collection in Data Centers and Its Analysis on Deep Learning-based Applications

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    Big data are usually stored in data center networks for processing and analysis through various cloud applications. Such applications are a collection of data-intensive jobs which often involve many parallel flows and are network bound in the distributed environment. The recent networking abstraction, coflow, for data parallel programming paradigm to express the communication requirements has opened new opportunities to network scheduling for such applications. Therefore, I propose coflow based network scheduling algorithm, Coflourish, to enhance the job completion time for such data-parallel applications, in the presence of the increased background traffic to mimic the cloud environment infrastructure. It outperforms Varys, the state-of-the-art coflow scheduling technique, by 75.5% under various workload conditions. However, such technique often requires customized operating systems, customized computing frameworks or external proprietary software-defined networking (SDN) switches. Consequently, in order to achieve the minimal application completion time, through coflow scheduling, coflow routing, and per-rate per-flow scheduling paradigm with minimum customization to the hosts and switches, I propose another scheduling technique, MinCOF which exploits the OpenFlow SDN. MinCOF provides faster deployability and no proprietary system requirements. It also decreases the average coflow completion time by 12.94% compared to the latest OpenFlow-based coflow scheduling and routing framework. Although the challenges related to analysis and processing of big data can be handled effectively through addressing the network issues. Sometimes, there are also challenges to analyze data effectively due to the limited data size. To further analyze such collected data, I use various deep learning approaches. Specifically, I design a framework to collect Twitter data during natural disaster events and then deploy deep learning model to detect the fake news spreading during such crisis situations. The wide-spread of fake news during disaster events disrupts the rescue missions and recovery activities, costing human lives and delayed response. My deep learning model classifies such fake events with 91.47% accuracy and F1 score of 90.89 to help the emergency managers during crisis. Therefore, this study focuses on providing network solutions to decrease the application completion time in the cloud environment, in addition to analyze the data collected using the deployed network framework to further use it to solve the real-world problems using the various deep learning approaches

    A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

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    Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page
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