Communities of online news exposure during the UK General Election 2015

Weaver, Iain S.; Williams, Hywel T. P.; Cioroianu, Iulia; Jasney, Lorien; Coan, Travis; Banducci, Susan

doi:10.1016/j.osnem.2019.05.001

Cited by 17 publications

(27 citation statements)

References 14 publications

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“…A similar phenomenon has been reported for farmers' markets, with a value of 0.221 [42], and for organic food, with a value of 0.303 [41]. On the contrary, the area of gamification shows traits of a polarized network, with a value of 0.506 [96], which is similar to the field of communication analysis in politics [97].…”

Section: Resultssupporting

confidence: 76%

Twitter Analysis of Global Communication in the Field of Sustainability

et al. 2019

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The increasing popularity of social media worldwide provides us with an opportunity to understand social, cultural, and environmental issues about people’s perception of sustainability. The article aims at identifying the main topics of communication related to hashtag # sustainability based on a communication analysis on the Twitter network. We investigated the perception of sustainability using data from 414,926 Twitter interactions by 223,476 users worldwide. The data were recorded between April 17, 2018 and July 12, 2019. We identified Innovation, Environment, Climate Change, Corporate Social Responsibility, Technology, and Energy as key hashtags in the field of sustainability. In conjunction with this, we identified the six following communities: (1) Environmental Sustainability, (2) Sustainability Awareness, (3) Renewable Energy and Climate Change, (4) Innovative Technology, (5) Green Architecture, and (6) Food Sustainability. The usage of these communities is applicable in marketing communication as well as in the Corporate Social Responsibility activities of the given companies. The results of the analysis give the organizations a possible direction for their sustainable business model improvement via the contribution of society´s voice.

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Section: Resultssupporting

confidence: 76%

Twitter Analysis of Global Communication in the Field of Sustainability

et al. 2019

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“…The lack of persistence in shared URLs may be explained by the general volatility of news media, where articles typically have a short lifetime (e.g. 2-3 days visibility in online sharing [ 13 ]). Moderate persistence of users and sources between weeks perhaps suggests an active core group who are present each week, with a wider group who appear less frequently.…”

Section: Resultsmentioning

confidence: 99%

“…These factors, along with the increasing use of social media as a source of news [ 12 ], mean that study of digital media sharing can provide insights into how information is propagated online, including important contemporary issues like climate change. In particular, Weaver et al [ 13 ] shows that network analysis of such propagation patterns can reveal meaningful social structures of news engagement and consumption around political events.…”

Section: Introductionmentioning

confidence: 99%

“…Williams et al [ 41 ] also study the community structure of political news-sharing via Twitter, finding communities characterised by both geographical and political factors. Weaver et al [ 13 ] examined information-sharing on Twitter during the UK General Election in 2015, showing strong community structure explained by ideological, geographical and topical preferences. Each of these studies should be considered in the context of the typical sharer and the information they are exposed to.…”

Section: Introductionmentioning

confidence: 99%

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Ideological biases in social sharing of online information about climate change

2021

Self Cite

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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.

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“…The authors used community detection algorithms to conclude that fake news reached a very niche and specialized audience, and did not feature prominently in mainstream news consumption behavior, at least among American voters who were active on Twitter. Yet other studies have used co-exposure networks of Twitter audiences to understand media consumption in contexts outside the US, such as in the UK 15 and in Asia 16 .…”

Section: Introductionmentioning

confidence: 99%

A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

Mukerjee

2021

Sci Rep

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The use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. I demonstrate how changing the manner in which co-exposure networks are constructed significantly improves the performances of some of these algorithms. Finally, I validate these findings using a novel empirical data-set of large-scale browsing behavior. The contributions of this research are two-fold: first, it shows that two specific algorithms, FastGreedy and Multilevel are the best suited for measuring selective exposure patterns in co-exposure networks. Second, it demonstrates the use of formal modeling for informing analytical choices for better capturing complex social phenomena.

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Communities of online news exposure during the UK General Election 2015

Cited by 17 publications

References 14 publications

Twitter Analysis of Global Communication in the Field of Sustainability

Twitter Analysis of Global Communication in the Field of Sustainability

Ideological biases in social sharing of online information about climate change

A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

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