CAREER: Enhanced Analysis & Algorithms to Minimize the Spread of Misinformation in Social Networks

职业：增强分析

• 批准号: 1943370
• 负责人: Francesca Spezzano
• 金额: $ 48.75万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943370&HistoricalAwards=false
• 关键词: CAREER; Enhanced; Analysis; & Algorithms

The project goal is to significantly reduce the destructive spread of misinformation on social media and other Web sources, and its potential threat to national security. To do so, the investigator will develop new machine learning algorithms to better detect authenticity and recommend content. The research integrates computer and social sciences to account for the complex real-world interactions among publisher, platform, content recommendation algorithms, bot users, human users, and their social connections. One way social networks engage users is by keeping them consuming personalized content. Malicious actors can easily penetrate these systems with misleading stories and consequent recommendations that prompt people to make decisions based on this misinformation. Younger generations are increasingly active on such platforms, making it critical to reduce the threat that the continuing spread of misinformation poses. Findings will result in a deeper understanding of how recommender systems behave in the presence of misleading stories, and will offer systems design strategies to insure that people receive accurate information to make decisions.There is currently no framework in place to quantify how much recommendations with misinformation in the loop influence social network users. This research will fill this gap. Project objectives are to: (1) develop graph-based models to measure the degree of story, sources, and user credibility as opposed to a typical binary assessment; (2) develop a new framework integrating user-centric information diffusion models to assess the impact of, and compute benchmarks for, current recommender systems in spreading misleading stories; (3) develop algorithms for content recommender systems that will minimize misinformation spread in social networks. An integrated education plan will engage Boise State University college students, who will use a service-learning approach to help Idaho high school students and teachers improve their ability to identify and respond to misinformation. Educational activities will also increase awareness of and interest in computer science occupations, and encourage minorities' retention and diversity.This project is jointly funded by Secure and Trustworthy Cyberspace (SaTC) program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是显著减少社交媒体和其他网络来源上错误信息的破坏性传播，以及对国家安全的潜在威胁。为此，研究人员将开发新的机器学习算法，以更好地检测真实性并推荐内容。该研究整合了计算机和社会科学，以解释出版商、平台、内容推荐算法、机器人用户、人类用户及其社交关系之间复杂的现实世界互动。社交网络吸引用户的一种方式是让他们消费个性化的内容。恶意行为者可以很容易地通过误导性的故事和随之而来的建议渗透到这些系统中，促使人们根据这些错误信息做出决定。年轻一代在这些平台上越来越活跃，因此必须减少错误信息持续传播造成的威胁。调查结果将导致更深入地了解如何推荐系统的行为存在误导性的故事，并将提供系统设计策略，以确保人们获得准确的信息，使decision.There目前还没有框架到位，以量化多少建议与错误信息的循环影响社交网络用户。这项研究将填补这一空白。项目目标是：（1）开发基于图的模型来衡量故事、来源和用户可信度的程度，而不是典型的二元评估;（2）开发一个新的框架，整合以用户为中心的信息传播模型，以评估当前推荐系统在传播误导性故事方面的影响，并计算其基准;（3）开发用于内容推荐系统的算法，其将最小化在社交网络中传播的错误信息。一项综合教育计划将吸引博伊西州立大学的大学生，他们将使用服务学习的方法来帮助爱达荷州高中学生和教师提高他们识别和应对错误信息的能力。教育活动还将提高对计算机科学职业的认识和兴趣，该项目由安全和值得信赖的网络空间（SaTC）计划和刺激竞争力研究的既定计划（EPSCoR）联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Using Service-Learning in Graduate Curriculum to Address Teenagers' Vulnerability to Web Misinformation

在研究生课程中利用服务学习来解决青少年对网络错误信息的脆弱性

• DOI: 10.1145/3456565.3460039
• 发表时间: 2021
• 期刊: ITiCSE '21: Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 2
• 作者: Spezzano, Francesca

How Do People Decide Political News Credibility?

人们如何决定政治新闻的可信度？

• DOI: 10.1109/asonam49781.2020.9381342
• 发表时间: 2020
• 期刊: ASONAM 2020
• 作者: Spezzano, Francesca;Winiecki, Don
• 通讯作者: Winiecki, Don

Characterizing and predicting fake news spreaders in social networks

• DOI: 10.1007/s41060-021-00291-z
• 发表时间: 2021-11-22
• 期刊: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
• 影响因子: 2.4
• 作者: Shrestha, Anu;Spezzano, Francesca
• 通讯作者: Spezzano, Francesca

Are you influenced?: modeling the diffusion of fake news in social media

• DOI: 10.1145/3487351.3488345
• 发表时间: 2021-11
• 期刊: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: Abishai Joy;Anu Shrestha;Francesca Spezzano

Textual Characteristics of News Title and Body to Detect Fake News: A Reproducibility Study

检测假新闻的新闻标题和正文的文本特征：再现性研究

• 发表时间: 2021
• 期刊: ECIR 2021
• 作者: Shrestha, Anu;Spezzano, Francesca
• 通讯作者: Spezzano, Francesca