Everything I disagree with is #FakeNews
Everything I disagree with is #FakeNews: Correlating political polarization and spread of misinformation
Author:
Manoel Horta Ribeiro, Federal University of Minas Gerais
Abstract:
An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the gap in the political views between people that engage with the so called "fake news". A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake. In this work, we study the relationship between political polarization and content reported by Twitter users as related to "fake news”. We investigate how polarization may create distinct narratives on what misinformation actually is. We perform our study based on two datasets collected from Twitter. The first dataset contains tweets about US politics in general, from which we compute the political leaning of each user towards the Republican and Democratic Party. In the second dataset, we collect tweets and URLs that co-occurred with "fake news" related keywords and hashtags, such as #FakeNews and #AlternativeFact, as well as reactions towards such tweets and URLs. We then analyze the relationship between polarization and what is perceived as misinformation, and whether users are designating information that they disagree as fake. Our results show an increase in the polarization of users and URLs (in terms of their associated political viewpoints) for information labeled with fakenews keywords and hashtags, when compared to information not labeled as "fake news". We discuss the impact of our findings on the challenges of tracking "fake news" in the ongoing battle against misinformation.
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео Everything I disagree with is #FakeNews канала KDD2017 video
Author:
Manoel Horta Ribeiro, Federal University of Minas Gerais
Abstract:
An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the gap in the political views between people that engage with the so called "fake news". A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake. In this work, we study the relationship between political polarization and content reported by Twitter users as related to "fake news”. We investigate how polarization may create distinct narratives on what misinformation actually is. We perform our study based on two datasets collected from Twitter. The first dataset contains tweets about US politics in general, from which we compute the political leaning of each user towards the Republican and Democratic Party. In the second dataset, we collect tweets and URLs that co-occurred with "fake news" related keywords and hashtags, such as #FakeNews and #AlternativeFact, as well as reactions towards such tweets and URLs. We then analyze the relationship between polarization and what is perceived as misinformation, and whether users are designating information that they disagree as fake. Our results show an increase in the polarization of users and URLs (in terms of their associated political viewpoints) for information labeled with fakenews keywords and hashtags, when compared to information not labeled as "fake news". We discuss the impact of our findings on the challenges of tracking "fake news" in the ongoing battle against misinformation.
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео Everything I disagree with is #FakeNews канала KDD2017 video
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Designing AI at Scale to Power Everyday LifeEstimation of Recent Ancestral Origins of Individuals on a Large Scalestruc2vec: Learning Node Representations from Structural IdentityLearning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical AttentionA Local Algorithm for StructurePreserving Graph CutMulti-Aspect Streaming Tensor CompletionVisualizing Deep Learning Activations for Improved Malaria Cell ClassificationPlanning Bike Lanes based on SharingBikes' TrajectoriesTripoles: A New Class of Relationships in Time Series DataIncorporating Feedback into Tree-based Anomaly DetectionIndustrial Machine LearningWeisfeiler-Lehman Neural Machine for Link PredictionOptimal Reserve Price for Online Ads Trading Based on Inventory IdentificationMulti-Aspect Streaming Tensor CompletionDispatch with Confidence: Integration of machine learningLearning certifiably optimal rule lists for categorical dataInterpretable Predictions of Tree-based Ensembles via Actionable Feature TweakingMultitask Learning using Task ClusteringThe Co-Evolution Model for Social Network Evolving and Opinion MigrationRevisiting power-law distributions in spectra of real world networksKDD Business Lunch