Ideological biases in social sharing of online information about climate change

This is the final version. Available on open access from Public Library of Science via the DOI in this record. Data availability: The external data used in this study (Twitter posts, news articles) was publicly available when the study was carried out, but since the original data is owned by third parties we cannot publish the complete dataset without infringing Terms of Use (Twitter) or copyright (news articles). Instead we have made available lists of sources (tweet IDs, news article URLs) so that interested parties can reproduce our work. Full details of the dataset and availability are given below. Figshare repository (http://doi.org/10.6084/m9.figshare.13554590) has been created with the data required to reproduce our results.Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/ right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.Engineering and Physical Sciences Research Council (EPSRC)Economic and Social Research Council (ESRC)Natural Environment Research Council (NERC

Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps

Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media

In this paper, we analyze content sharing between news sources in the alternative and mainstream media using a dataset of 713K articles and 194 sources. We find that content sharing happens in tightly formed communities, and these communities represent relatively homogeneous portions of the media landscape. Through a mix-method analysis, we find several primary content sharing behaviors. First, we find that the vast majority of shared articles are only shared with similar news sources (i.e. same community). Second, we find that despite these echo-chambers of sharing, specific sources, such as The Drudge Report, mix content from both mainstream and conspiracy communities. Third, we show that while these differing communities do not always share news articles, they do report on the same events, but often with competing and counter-narratives. Overall, we find that the news is homogeneous within communities and diverse in between, creating different spirals of sameness.Comment: Published at ICWSM 201

User sentiment detection: a YouTube use case

In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis