توضیحاتی در مورد کتاب Disinformation in Open Online Media: Third Multidisciplinary International Symposium, MISDOOM 2021, Virtual Event, September 21–22, 2021, Proceedings
نام کتاب : Disinformation in Open Online Media: Third Multidisciplinary International Symposium, MISDOOM 2021, Virtual Event, September 21–22, 2021, Proceedings
عنوان ترجمه شده به فارسی : اطلاعات نادرست در رسانه های آنلاین باز: سومین سمپوزیوم بین المللی چند رشته ای، MISDOOM 2021، رویداد مجازی، 21 تا 22 سپتامبر 2021، مجموعه مقالات
سری : Lecture Notes in Computer Science; 12887
نویسندگان : Jonathan Bright
ناشر : Springer
سال نشر : 2021
تعداد صفحات : 161
ISBN (شابک) : 3030870308 , 9783030870300
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 11 مگابایت
بعد از تکمیل فرایند پرداخت لینک دانلود کتاب ارائه خواهد شد. درصورت ثبت نام و ورود به حساب کاربری خود قادر خواهید بود لیست کتاب های خریداری شده را مشاهده فرمایید.
فهرست مطالب :
Preface
Organization
Keynotes
Russian Disinformation, Five Years Later
Psychological Inoculation Against Misinformation
Computational Challenges and Recent Advancements in Automated Fake News Detection
Contents
The Explanatory Gap in Algorithmic News Curation
1 Introduction
2 Background
3 Method
3.1 Sampling and Participants
3.2 Explanations for ML-Based Curation System
4 Results
5 Discussion
6 Conclusion
References
Examining Linguistic Biases in Telegram with a Game Theoretic Analysis
1 Introduction
2 The Basic Model and Method
3 The Formal Model of Bias
4 Analysing Linguistic Bias with ME Games
4.1 Identities of Protesters and Police
4.2 The ``Neutral\'\' View Point
4.3 Interaction Between Protester and Police
4.4 Dynamics and Bias Hardening
5 Discussion and Conclusions
References
Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data
1 Introduction
2 Related Work
3 Methodology
3.1 Data Collection and Preprocessing
3.2 Sentiment Analysis
3.3 Topic Modelling
3.4 Time Series Generation
3.5 Structural Break Models
4 Application on COVID-19 Related Tweets
4.1 Encoding Sentiment
4.2 Establishing Topics
4.3 Event Detection with Structural Break Models
4.4 Detecting Sentiment Changes in User Discussions
5 Conclusion
References
Is YouTube Still a Radicalizer? An Exploratory Study on Autoplay and Recommendation
1 Introduction
2 Related Work
2.1 Analyzing How the YouTube Recommender Might Work
2.2 Issues with Recommendations in YouTube
3 Experimental Design
4 Experiments
5 Discussion and Conclusion
References
Understanding the Impact of and Analysing Fake News About COVID-19 in SA
1 Introduction
1.1 Study Context
2 Related Work
2.1 Fake News
2.2 Theories of Fake News
2.3 Fake News on Social Media and the Knowledge Gap
3 Methodology
3.1 Data Collection
3.2 Data Processing
3.3 Data Verification
3.4 Data Annotation
4 Analysis
5 Discussion
5.1 Findings
5.2 Limitations and Future Work
6 Conclusion
References
A Study of Misinformation in Audio Messages Shared in WhatsApp Groups
1 Introduction
2 Related Work
3 Methodology
3.1 WhatsApp Dataset
3.2 Misinformation Detection
4 Content Analysis
4.1 Topic Analysis
4.2 Psychological Linguistic Features
4.3 Qualitative Analysis
5 Propagation Dynamics
6 Conclusions and Future Work
References
Hide and Seek in Slovakia: Utilizing Tracking Code Data to Uncover Untrustworthy Website Networks
1 Introduction
1.1 Related Work
1.2 The Slovak Untrustworthy Website Landscape
2 Methods
3 Results
3.1 Sample Description of Two Uncovered Networks
4 Discussion
References
The German Comment Landscape
1 Introduction
2 Research Background
2.1 Audience Pariticipation in Digital Newspapers
2.2 Previous Research on the State of Participatory Options
3 Method
3.1 Sample Selection
3.2 Analysed Aspects
4 Analysis
4.1 General Findings
4.2 Findings by Newspaper Size
5 Discussion
6 Conclusion and Limitations
References
Evaluating the Role of News Content and Social Media Interactions for Fake News Detection
1 Introduction
2 Related Work
3 Model and Feature Extraction
3.1 Content-Based Features
3.2 Engagement Features
4 Method and Dataset
5 Data Analysis and Findings – In Two Distinctive Phases
5.1 Phase A - Evaluating the Importance of the Proposed Model Content-Based Features
5.2 Phase B - Combining the Content-Based Features with the Engagement Features
6 Discussion of the Results
7 Conclusion and Future Work
References
Author Index