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EAGER: Towards a Better Understanding of Group Privacy in Social Media Community Detection

EAGER：更好地理解社交媒体社区检测中的群体隐私

基本信息

批准号：
1649469
负责人：
Amr El Abbadi
金额：
$ 15.8万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649469&HistoricalAwards=false
关键词：
EAGER Towards Better Understanding Group

项目摘要

Much of human communication is now mediated by online social networks. Twitter, Facebook, and Youtube now compete for our collective attention in much the same way as television, radio, and newspapers did for previous generations. But contemporary online social media are qualitatively different from media of the past. Online communication leaves a record of who said what to whom, when, and on what topic. The development of new analytical tools offer the possibility to use these records to track popular on-line topics and to identify the demographics of groups contributing to these topics, including the geographic location of contributors, as well as their age, gender, and ethnicity. What is more, it is possible for ad hoc groups to coalesce around topics in real time. On the one hand, these data present a challenge for computer scientists to develop new tools that enable the tracking of these kinds of information. Success in this domain offers significant practical benefits in business, marketing, and politics. At the same time, however, the ability to track these kinds of information raise privacy concerns, both for individuals and for members of groups who can be identified by the emerging technology. In our research, computer scientists will develop tools that enable tracking of topics and group memberships, and communication researchers will identify the kinds of privacy concerns that people develop around these kinds of information, when, why, and with what consequences.The research described in this project builds on prior work on trend analysis and community extraction, seeking to advance research on two fronts: the efficient identification of ad hoc communities which focus on a popular topic, and the understanding of individual versus group privacy based on the identified communities. Although trend analysis is a burgeoning area of research in Computer Science, existing models focus on the correlation of only one dimension (e.g., location) with trending topics. This research represents a step forward by providing a more sophisticated and powerful tool that allows for the extraction of interesting and potentially useful trend patterns through Topic Based Community Identification. At the same time, this community identification may prompt new types of group privacy concerns that have not been researched in social science, which has mainly focused on individual rather than group privacy. This approach provides a unique opportunity to significantly impact scholarly understanding of the mechanisms and dynamics of individual and group privacy concern, especially with regard to ad-hoc, topic-based groups, and their effects on Social Media users' attitudes and behavior. If group-level privacy is a concern beyond individual privacy, then we expect to find that people will express group privacy concerns when a group they identify with is included in the tracking information, especially when topics are morally loaded, but independent of whether the individual participant is personally involved in the Twitter conversation. This has the potential to develop necessary and sufficient conditions for the emergence of group privacy concerns.

现在，人类的大部分交流都是通过在线社交网络进行的。Twitter、Facebook和YouTube现在争夺我们的集体关注，就像电视、广播和报纸对前几代人所做的那样。但当代在线社交媒体与过去的媒体有质的不同。在线交流留下了谁对谁说了什么的记录，什么时候说了什么，说了什么话题。新的分析工具的开发使人们有可能利用这些记录来跟踪热门的在线主题，并确定促成这些主题的群体的人口统计数据，包括撰稿人的地理位置以及他们的年龄、性别和族裔。更重要的是，特设小组可以实时围绕主题进行联合。一方面，这些数据对计算机科学家来说是一个挑战，他们需要开发新的工具来跟踪这些类型的信息。在这一领域的成功在商业、营销和政治上都会带来巨大的实际利益。然而，与此同时，追踪这类信息的能力引发了隐私问题，无论是对个人还是对可以通过这项新兴技术识别的群体成员来说都是如此。在我们的研究中，计算机科学家将开发能够跟踪主题和群组成员身份的工具，而传播研究人员将确定人们围绕这些类型的信息产生的隐私担忧的种类，何时，为什么，以及产生什么后果。本项目中描述的研究建立在趋势分析和社区提取的先前工作的基础上，寻求在两个方面推进研究：高效地识别专注于流行主题的临时社区，以及基于识别的社区理解个人和群体隐私。虽然趋势分析是计算机科学中一个新兴的研究领域，但现有的模型只关注一个维度(例如，位置)与趋势主题的相关性。这项研究代表着向前迈进了一步，提供了一个更复杂和更强大的工具，允许通过基于主题的社区识别来提取有趣的和潜在有用的趋势模式。与此同时，这种社区认同可能会引发社会科学尚未研究的新类型的群体隐私问题，社会科学主要关注个人隐私，而不是群体隐私。这种方法提供了一个独特的机会，可以显著影响学术界对个人和群体隐私问题的机制和动态的理解，特别是关于临时的、基于话题的群体，以及它们对社交媒体用户态度和行为的影响。如果组级别隐私是个人隐私之外的一个问题，那么我们预计会发现，当他们认同的组被包括在跟踪信息中时，特别是当话题从道德上加载时，人们会表达组隐私问题，但与个人参与者是否亲自参与Twitter对话无关。这有可能为群体隐私问题的出现创造必要和充分的条件。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Extracting Topics with Focused Communities for Social Content Recommendation

DOI：
10.1145/2998181.2998259
发表时间：
2017-02
期刊：
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
影响因子：
0
作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan
通讯作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan

Privacy Cyborg: Towards Protecting the Privacy of Social Media Users

DOI：
10.1109/icde.2017.193
发表时间：
2017-04
期刊：
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
影响因子：
0
作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan
通讯作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan

Pharos: Privacy Hazards of Replicating ORAM Stores

Pharos：复制 ORAM 存储的隐私危害

DOI：
10.5441/002/edbt.2018.89
发表时间：
2018
期刊：
EDBT 2018
影响因子：
0
作者：
Zakhary, Victor;Sahin, Cetin;El Abbadi, Amr;Lin, Huijia;Tessaro, Stefano
通讯作者：
Tessaro, Stefano

LocBorg: Hiding Social Media User Location while Maintaining Online Persona

LocBorg：隐藏社交媒体用户位置，同时维护在线角色

DOI：
10.1145/3139958.3140057
发表时间：
2017
期刊：
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
Victor Zakhary;Cetin Sahin;T. Georgiou;A. E. Abbadi
通讯作者：
A. E. Abbadi

Distinguishing Group Privacy From Personal Privacy: The Effect of Group Inference Technologies on Privacy Perceptions and Behaviors

区分群体隐私和个人隐私：群体推理技术对隐私认知和行为的影响