1st international workshop on user modeling from social media

J. Mahmud, Jeffrey Nichols, Michelle X. Zhou
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Abstract

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. People from all walks of life share data about social events, express opinions, discuss their interests, publicize businesses, recommend products, and, explicitly or implicitly, reveal personal information. This workshop will focus on the use of social media data for creating models of individual users from the content that they publish. Deeper understanding of user behavior and associated attributes can benefit a wide range of intelligent applications, such as social recommender systems and expert finders, as well as provide the foundation in support of novel user interfaces (e.g., actively engaging the crowd in mixed-initiative question-answering systems). These applications and interfaces may offer significant benefits to users across a wide variety of domains, such as retail, government, healthcare and education. User modeling from public social media data may also reveal information that users would prefer to keep private. Such concerns are particularly important because individuals do not have complete control over the information they share about themselves. For example, friends of a user may inadvertently divulge private information about that user in their own posts. In this workshop we will also discuss possible mechanisms that users might employ to monitor what information has been revealed about themselves on social media and obfuscate any sensitive information that has been accidentally revealed.
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第一届社交媒体用户建模国际研讨会
Twitter和Facebook等社交媒体网站正在产生大量数据。各行各业的人们分享有关社会事件的数据,表达意见,讨论他们的兴趣,宣传业务,推荐产品,并或明或暗地透露个人信息。本次研讨会将重点讨论如何使用社交媒体数据,根据个人用户发布的内容创建他们的模型。对用户行为和相关属性的更深入理解可以有利于广泛的智能应用程序,例如社会推荐系统和专家查找器,以及为支持新颖的用户界面(例如,在混合主动问答系统中积极参与人群)提供基础。这些应用程序和接口可以为零售、政府、医疗保健和教育等广泛领域的用户提供显著的好处。基于公共社交媒体数据的用户建模也可能揭示用户希望保密的信息。这种担忧尤其重要,因为个人无法完全控制他们分享的关于自己的信息。例如,用户的朋友可能在他们自己的帖子中无意中泄露了该用户的私人信息。在本次研讨会中,我们还将讨论用户可能采用的机制,以监控社交媒体上泄露的有关自己的信息,并混淆意外泄露的任何敏感信息。
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IUI 2022: 27th International Conference on Intelligent User Interfaces, Helsinki, Finland, March 22 - 25, 2022 Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier IUI '21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021 Towards Making Videos Accessible for Low Vision Screen Magnifier Users. SaIL: Saliency-Driven Injection of ARIA Landmarks.
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