Modelling the distance impedance of protest attendance

Traag, Vincent; Quax, Rick; Sloot, Peter M. A.

doi:10.1016/j.physa.2016.10.054

Cited by 11 publications

(11 citation statements)

References 39 publications

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“…The concern that news and social media estimates of protest size cannot be trusted should be laid to rest. Though others have reported protest size using cellphone records, we are the first, as far as we are aware, to use location data from them as opposed to call detail records collected by transmission towers (Shalev and Rotman 2020;Traag, Quax, and Sloot 2017) and to verify secondary datasets. Whether a researcher prefers news or social media to measure protest size variation is his or her choice, as they are equally accurate.…”

Section: Concluding Thoughtsmentioning

confidence: 99%

News and Geolocated Social Media Accurately Measure Protest Size Variation

et al. 2020

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Larger protests are more likely to lead to policy changes than small ones are, but whether or not attendance estimates provided in news or generated from social media are biased is an open question. This letter closes the question: news and geolocated social media data generate accurate estimates of protest size variation. This claim is substantiated using cellphone location data from more than 10 million individuals during the 2017 United States Women’s March protests. These cellphone estimates correlate strongly with those provided in news media as well as three size estimates generated using geolocated tweets, one text-based and two based on images. Inferences about protest attendance from these estimates match others’ findings about the Women’s March.

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Section: Concluding Thoughtsmentioning

confidence: 99%

News and Geolocated Social Media Accurately Measure Protest Size Variation

et al. 2020

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“…such as inequality or grievances in a given city) that could contribute to explain the willingness to support a given protest using social media tools. Among these elements, it has also been studied whether participation in protests declines with distance to the end point of the march (Traag et al 2017). However, to the best of our knowledge, a combined exploration of all these types of variables (i.e., neighborhood attributes, pedestrian networks, individual covariates, and the physical distance to the demonstration march) in a subpopulation of social media users has not been previously carried out.…”

Section: Comparison To Previous Researchmentioning

confidence: 99%

Detecting social media users based on pedestrian networks and neighborhood attributes: an observational study

et al. 2019

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This paper proposes a methodological approach to explore the ability to detect social media users based on pedestrian networks and neighborhood attributes. We propose the use of a detection function belonging to the Spatial Capture-Recapture (SCR) which is a powerful analytical approach for detecting and estimating the abundance of biological populations. To test our approach, we created a set of proxy measures for the importance of pedestrian streets as well as neighborhood attributes. The importance of pedestrian streets was measured by centrality indicators. Additionally, proxy measures of neighborhood attributes were created using multivariate analysis of census data. A series of candidate models were tested to determine which attributes are most important for detecting social media users. The results of the analysis provide information on which attributes of the city have promising potential for detecting social media users. Finally, the main results and findings, limitations and extended use of the proposed methodological approach are discussed.

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“…Geographical distances among the nodes of the network usually come in the form of Tinbergen’s gravity model 38 . Manifestations of spatial embedding of networks 39 are truly omnipotent, ranging from the original inter-country trade 40 , 41 through inter-city telecommunication flows 42 and online friendship 43 to active protesters 44 . In the case of scientific collaboration Pan et al .…”

Section: Discussionmentioning

confidence: 99%

Categorical and Geographical Separation in Science

Sienkiewicz

Soja

Hołyst

et al. 2018

Sci Rep

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We study scientific collaboration at the level of universities. The scope of this study is to answer two fundamental questions: (i) can one indicate a category (i.e., a scientific discipline) that has the greatest impact on the rank of the university and (ii) do the best universities collaborate with the best ones only? Restricting ourselves to the 100 best universities from year 2009 we show how the number of publications in certain categories correlates with the university rank. Strikingly, the expected negative trend is not observed in all cases – for some categories even positive values are obtained. After applying Principal Component Analysis we observe clear categorical separation of scientific disciplines, dividing the papers into almost separate clusters connected to natural sciences, medicine and arts and humanities. Moreover, using complex networks analysis, we give hints that the scientific collaboration is still embedded in the physical space and the number of common papers decays with the geographical distance between them.

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Modelling the distance impedance of protest attendance

Cited by 11 publications

References 39 publications

News and Geolocated Social Media Accurately Measure Protest Size Variation

News and Geolocated Social Media Accurately Measure Protest Size Variation

Detecting social media users based on pedestrian networks and neighborhood attributes: an observational study

Categorical and Geographical Separation in Science

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