@Article{duepublico_mods_00070827, author = {Masias, Victor H. and Hecking, Tobias and Crespo, Fernando and Hoppe, H. Ulrich}, title = {Detecting social media users based on pedestrian networks and neighborhood attributes: an observational study}, year = {2019}, month = {Oct}, day = {29}, keywords = {Mexico city, Social media, Pedestrian networks, Socio-demographic attributes, User behaviour, Protest march, Mixed methods}, abstract = {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.}, note = {Applied Network Science (2019) 4:96; Published 29 October 2019 - corrected publication 2019}, note = {<p>The publication of this article was supported by the Publication Fund of the University of Duisburg-Essen.</p>}, doi = {10.1007/s41109-019-0222-4}, url = {https://duepublico2.uni-due.de/receive/duepublico_mods_00070827}, url = {https://doi.org/10.1007/s41109-019-0222-4}, file = {:https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00070827/Masias_et_al_Detecting_social_media_users.pdf:PDF}, language = {en} }