On spatial variation in the detectability and density of social media
user protest supporters
- URL: http://arxiv.org/abs/2103.06063v1
- Date: Wed, 10 Mar 2021 14:08:08 GMT
- Title: On spatial variation in the detectability and density of social media
user protest supporters
- Authors: V\'ictor H. Mas\'ias, Fernando Crespo, Pilar Navarro R., Razan Masood,
Nicole C. Kr\"amer, and H. Ulrich Hoppe
- Abstract summary: The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users' spatial detectability and density during a protest in Mexico City.
- Score: 52.77024349608834
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although much has been published regarding street protests on social media,
few works have attempted to characterize social media users' spatial behavior
in such events. The research reported here uses spatial capture-recapture
methods to determine the influence of the built environment, physical proximity
to protest location, and collective posting rhythm on variations in users'
spatial detectability and density during a protest in Mexico City. The
best-obtained model, together with explaining the spatial density of users,
shows that there is high variability in the detectability of social media user
protest supporters and that the collective posting rhythm and the day of
observation are significant explanatory factors. The implication is that
studies of collective spatial behavior would benefit by focussing on users'
activity centres and their urban environment, rather than their physical
proximity to the protest location, the latter being unable to adequately
explain spatial variations in users' detectability and density during the
protest event.
Related papers
- Instantaneous Perception of Moving Objects in 3D [86.38144604783207]
The perception of 3D motion of surrounding traffic participants is crucial for driving safety.
We propose to leverage local occupancy completion of object point clouds to densify the shape cue, and mitigate the impact of swimming artifacts.
Extensive experiments demonstrate superior performance compared to standard 3D motion estimation approaches.
arXiv Detail & Related papers (2024-05-05T01:07:24Z) - ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection [70.11264880907652]
Recent object (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios.
We propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and camouflaged zooming in and out.
Our framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks.
arXiv Detail & Related papers (2023-10-31T06:11:23Z) - Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
A fundamental limitation of object detectors is that they suffer from "spatial bias"
We present a new zone evaluation protocol, which measures the detection performance over zones.
For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
arXiv Detail & Related papers (2023-10-20T01:44:49Z) - Towards Spatial Equilibrium Object Detection [88.9747319572368]
In this paper, we study the spatial disequilibrium problem of modern object detectors.
We propose to quantify this problem by measuring the detection performance over zones.
This motivates us to design a more generalized measurement, termed Spatial equilibrium Precision.
arXiv Detail & Related papers (2023-01-14T17:33:26Z) - Location retrieval using visible landmarks based qualitative place
signatures [0.7119463843130092]
A qualitative location retrieval method is proposed in this work by describing locations/places using qualitative place signatures (QPS)
After dividing the space into place cells each with individual signatures attached, a coarse-to-fine location retrieval method is proposed to efficiently identify the possible location(s) of viewers based on their qualitative observations.
arXiv Detail & Related papers (2022-07-26T13:57:49Z) - BEV-Net: Assessing Social Distancing Compliance by Joint People
Localization and Geometric Reasoning [77.08836528980248]
Social distancing, an essential public health measure, has gained significant attention since the outbreak of the COVID-19 pandemic.
In this work, the problem of visual social distancing compliance assessment in busy public areas with wide field-of-view cameras is considered.
A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced.
A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated.
arXiv Detail & Related papers (2021-10-10T23:56:37Z) - Geolocation differences of language use in urban areas [0.0]
We explore the use of Twitter data with precise geolocation information to resolve spatial variations in language use on an urban scale down to single city blocks.
Our work shows that analysis of small-scale variations can provide unique information on correlations between language use and social context.
arXiv Detail & Related papers (2021-08-01T19:55:45Z) - Real-time Spatio-temporal Event Detection on Geotagged Social Media [3.446756313739598]
We propose an online event detection system using social media to detect events at different time and space resolutions.
A post processing stage is introduced to filter out events that are spam, fake or wrong.
The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York.
arXiv Detail & Related papers (2021-06-23T07:14:03Z) - Monitoring Social-distance in Wide Areas during Pandemics: a Density Map
and Segmentation Approach [0.0]
We propose a new framework for monitoring the social-distance using end-to-end Deep Learning.
Our framework consists in the creation of a new ground truth based on the ground truth density maps.
We show that our framework performs well at providing the zones where people are not following the social-distance even when heavily occluded or far away from one camera.
arXiv Detail & Related papers (2021-04-07T19:26:26Z) - Exposure Density and Neighborhood Disparities in COVID-19 Infection
Risk: Using Large-scale Geolocation Data to Understand Burdens on Vulnerable
Communities [1.2526963688768453]
This study develops a new method to quantify neighborhood activity levels at high spatial and temporal resolutions.
We define exposure density as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in non-residential and outdoor land uses.
arXiv Detail & Related papers (2020-08-04T15:41:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.