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Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
arXiv - CS - Social and Information Networks Pub Date : 2020-08-26 , DOI: arxiv-2009.07632 Christian von der Weth, Ashraf Abdul, Shaojing Fan, Mohan Kankanhalli
arXiv - CS - Social and Information Networks Pub Date : 2020-08-26 , DOI: arxiv-2009.07632 Christian von der Weth, Ashraf Abdul, Shaojing Fan, Mohan Kankanhalli
Participation on social media platforms has many benefits but also poses
substantial threats. Users often face an unintended loss of privacy, are
bombarded with mis-/disinformation, or are trapped in filter bubbles due to
over-personalized content. These threats are further exacerbated by the rise of
hidden AI-driven algorithms working behind the scenes to shape users' thoughts,
attitudes, and behavior. We investigate how multimedia researchers can help
tackle these problems to level the playing field for social media users. We
perform a comprehensive survey of algorithmic threats on social media and use
it as a lens to set a challenging but important research agenda for effective
and real-time user nudging. We further implement a conceptual prototype and
evaluate it with experts to supplement our research agenda. This paper calls
for solutions that combat the algorithmic threats on social media by utilizing
machine learning and multimedia content analysis techniques but in a
transparent manner and for the benefit of the users.
中文翻译:
帮助用户应对社交媒体上的算法威胁:多媒体研究议程
参与社交媒体平台有很多好处,但也带来了巨大的威胁。用户经常面临意外的隐私丢失,被错误/虚假信息轰炸,或者由于过度个性化的内容而被困在过滤气泡中。这些威胁因隐藏的人工智能驱动算法的兴起而进一步加剧,这些算法在幕后工作以塑造用户的思想、态度和行为。我们调查多媒体研究人员如何帮助解决这些问题,为社交媒体用户提供公平的竞争环境。我们对社交媒体上的算法威胁进行了全面调查,并将其用作镜头,为有效和实时的用户推动设定具有挑战性但重要的研究议程。我们进一步实施了一个概念原型,并与专家一起对其进行评估,以补充我们的研究议程。
更新日期:2020-09-17
中文翻译:
帮助用户应对社交媒体上的算法威胁:多媒体研究议程
参与社交媒体平台有很多好处,但也带来了巨大的威胁。用户经常面临意外的隐私丢失,被错误/虚假信息轰炸,或者由于过度个性化的内容而被困在过滤气泡中。这些威胁因隐藏的人工智能驱动算法的兴起而进一步加剧,这些算法在幕后工作以塑造用户的思想、态度和行为。我们调查多媒体研究人员如何帮助解决这些问题,为社交媒体用户提供公平的竞争环境。我们对社交媒体上的算法威胁进行了全面调查,并将其用作镜头,为有效和实时的用户推动设定具有挑战性但重要的研究议程。我们进一步实施了一个概念原型,并与专家一起对其进行评估,以补充我们的研究议程。