456 research outputs found

    Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts

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    So-called 'social bots' have garnered a lot of attention lately. Previous research showed that they attempted to influence political events such as the Brexit referendum and the US presidential elections. It remains, however, somewhat unclear what exactly can be understood by the term 'social bot'. This paper addresses the need to better understand the intentions of bots on social media and to develop a shared understanding of how 'social' bots differ from other types of bots. We thus describe a systematic review of publications that researched bot accounts on social media. Based on the results of this literature review, we propose a scheme for categorising bot accounts on social media sites. Our scheme groups bot accounts by two dimensions - Imitation of human behaviour and Intent.Comment: Accepted for publication in the Proceedings of the Australasian Conference on Information Systems, 201

    Understanding the Detection of View Fraud in Video Content Portals

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    While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators. In this paper we present a first set of tools to serve this purpose. Using our tools, we evaluate the performance of the audit systems of five major online video portals. Our results reveal that YouTube's detection system significantly outperforms all the others. Despite this, a systematic evaluation indicates that it may still be susceptible to simple attacks. Furthermore, we find that YouTube penalizes its videos' public and monetized view counters differently, the former being more aggressive. This means that views identified as fake and discounted from the public view counter are still monetized. We speculate that even though YouTube's policy puts in lots of effort to compensate users after an attack is discovered, this practice places the burden of the risk on the advertisers, who pay to get their ads displayed.Comment: To appear in WWW 2016, Montr\'eal, Qu\'ebec, Canada. Please cite the conference version of this pape

    Strategies and Influence of Social Bots in a 2017 German state election

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    This study aims to examine the influence of environmental and personal factors on knowledge-sharing behaviour (KSB) and whether more leads to superior innovative work behaviour (IWB) at tertiary level in Vietnam. Our case is Hanoi University (HANU), one of the Leading Public Universities in Vietnam. This study applies the structural equation modelling (SEM) to investigate the research model based on social cognitive theory. Based on a survey of 320 academic staff at HANU, the results show that two environmental factors (subjective norm, trust) and three personal factors (knowledge self-efficacy, enjoyment in helping others, and reciprocity) significantly influence KSB. The results also indicate that employee willingness to share knowledge enable the organisation to promote IWB. It is hoped that academic staff and university leaders from other countries may find the case study useful for deeper understanding of the effects of social influences, personal perceptions and KSB on IBW in the future
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