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000844297 019__ $$a1046101442$$a1046646033$$a1047595277$$a1047776782
000844297 020__ $$a9783319785837$$q(electronic book)
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000844297 050_4 $$aHV6773.15.C92
000844297 08204 $$a302.343$$223
000844297 24500 $$aOnline harassment /$$cJennifer Golbeck, editor.
000844297 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000844297 300__ $$a1 online resource.
000844297 336__ $$atext$$btxt$$2rdacontent
000844297 337__ $$acomputer$$bc$$2rdamedia
000844297 338__ $$aonline resource$$bcr$$2rdacarrier
000844297 4901_ $$aHuman-computer interaction series
000844297 5050_ $$aIntro; Contents; Contributors; 1 Online Harassment: A Research Challenge for HCI; Detection; 2 Weak Supervision and Machine Learning for Online Harassment Detection; 2.1 Introduction; 2.2 Related Work; 2.2.1 Machine Learning for Detection of Online Harassment and Related Phenomena; 2.2.2 Weakly Supervised Machine Learning; 2.3 Participant-Vocabulary Consistency; 2.3.1 Model Details; 2.3.2 Learning the Parameters; 2.4 Experiments; 2.4.1 Data Processing; 2.4.2 Baselines; 2.4.3 Human Annotation Comparisons; 2.4.4 Qualitative Analysis; 2.5 Discussion, Extensions, and Open Problems
000844297 5058_ $$a2.5.1 Deep Learning2.5.2 Fairness; 2.5.3 Weak Supervision Interface; 2.5.4 Automated Interventions; References; 3 Bridging the Gaps: Multi Task Learning for Domain Transfer of Hate Speech Detection; 3.1 Introduction; 3.1.1 Hate Speech Detection; 3.1.2 Multi-task Learning; 3.1.3 Utility of Multi-task Learning for Hate Speech Detection; 3.2 Data; 3.2.1 Understandings of ``Hate Speech''; 3.2.2 Commonalities and Differences; 3.3 Model; 3.3.1 Baseline Model Definition; 3.3.2 Multi-task Model Definition; 3.3.3 Training; 3.3.4 Features; 3.3.5 Pre-processing; 3.4 Experiments; 3.4.1 Baseline Models
000844297 5058_ $$a3.4.2 Composite Data Models3.4.3 Multi-task Learning Models; 3.4.4 Dataset Statistics; 3.4.5 Evaluation Metrics; 3.5 Experimental Results; 3.5.1 Single-Task Baseline Models; 3.5.2 Composite Dataset Models; 3.5.3 Multi-task Learning Models; 3.5.4 Critiques of Datasets; 3.6 Related Work; 3.6.1 Abusive Language; 3.6.2 Multi-task Learning; 3.7 Conclusion; 3.8 Future Work; References; 4 A Network Analysis of the GamerGate Movement; 4.1 The Network; 4.2 #OpSkynet; 4.3 #NotYourShield; 4.4 #SJWs; 4.5 #GamerGate; 4.6 Signal Boosters (Edge Class 0); 4.6.1 Structural Analysis; 4.6.2 Content Analysis
000844297 5058_ $$a4.6.3 Conclusions: Edge Class 04.7 Flag Bearers (Edge Class 1); 4.7.1 Structural Analysis; 4.7.2 Content Analysis; 4.7.3 Conclusions: Edge Class 1; 4.8 Activists and Advocates (Edge Class 2); 4.8.1 Structural Analysis; 4.8.2 Content Analysis; 4.8.3 R/TheOpenHouse (@Rsolgtp); 4.8.4 I'm a Consumer (@Imaconsumer); 4.8.5 Ethan2478 (@GuidesGame); 4.8.6 A Man in Camouflage (@the_Sgt_Maj), Lalafell Warrior X (@Fuzzytoad), and Frankie Sweets @TheSweetsTweet; 4.8.7 Conclusions; 4.9 Generals (Edge Class 4); 4.9.1 Structural Analysis; 4.9.2 Content Analysis; 4.9.3 Conclusions: Edge Class 4
000844297 5058_ $$a4.10 Soldiers (Edge Class 5)4.10.1 Structural Analysis; 4.10.2 Content Analysis; 4.11 The Spectrum (Edge Class 6); 4.11.1 Structural Analysis; 4.11.2 Content Analysis; 4.11.3 Conclusions; 4.12 Overall Conclusions; 4.12.1 No Single Narrative; 4.12.2 Post-feminist Leadership; 4.12.3 Reports of My Death…; 4.12.4 Directions for Future Study; References; 5 Automation and Harassment Detection; 5.1 Introduction; 5.2 First Attempt: Just Code; 5.3 A Little Help from Statistics; 5.4 A First Attempt with Machine Learning; 5.5 Improving Our Model; 5.6 Domain Matters: A Tail of Two Distributions
000844297 506__ $$aAccess limited to authorized users.
000844297 520__ $$aOnline Harassment is one of the most serious problems in social media. To address it requires understanding the forms harassment takes, how it impacts the targets, who harasses, and how technology that stands between users and social media can stop harassers and protect users. The field of Human-Computer Interaction provides a unique set of tools to address this challenge. This book brings together experts in theory, socio-technical systems, network analysis, text analysis, and machine learning to present a broad set of analyses and applications that improve our understanding of the harassment problem and how to address it. This book tackles the problem of harassment by addressing it in three major domains. First, chapters explore how harassment manifests, including extensive analysis of the Gamer Gate incident, stylistic features of different types of harassment, how gender differences affect misogynistic harassment. Then, we look at the results of harassment, including how it drives people offline and the impacts it has on targets. Finally, we address techniques for mitigating harassment, both through automated detection and filtering and interface options that users control. Together, many branches of HCI come together to provide a comprehensive look at the phenomenon of online harassment and to advance the field toward effective human-oriented solutions.
000844297 588__ $$aOnline resource; title from PDF title page (viewed July 25, 2018).
000844297 650_0 $$aCyberbullying.
000844297 650_0 $$aHarassment.
000844297 650_0 $$aInternet$$xSocial aspects.
000844297 7001_ $$aGolbeck, Jennifer,$$eeditor.
000844297 77608 $$iPrint version: $$z3319785826$$z9783319785820$$w(OCoLC)1024210622
000844297 830_0 $$aHuman-computer interaction series.
000844297 852__ $$bebk
000844297 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-78583-7$$zOnline Access$$91397441.1
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