Geocoding Without Geotags: A Text-based Approach for reddit

Harrigian, Keith

doi:10.48550/arxiv.1810.03067

Cited by 2 publications

(3 citation statements)

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“…Text features and network information can identify user attribute parameters [119]. Location tags for Reddit users can be generated despite the absence of explicit geotagging data [120]. By mining the common characteristics of the same user from different networks, users can be matched in heterogeneous social networks.…”

Section: B Social Network Analysismentioning

confidence: 99%

Geosocial Media as a Proxy for Security: A Review

Han¹,

Li²,

Cui³

et al. 2022

Preprint

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<p>Since the rapid growth of urban populations, the study of urban systems has gained considerable attention from researchers, decision makers, governments, and organizations. Urban systems are complex and dynamic such that they produce emergent patterns such as self-organization and nonlinearity. Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities. However, agent-based models are difficult to effectively optimize and validate without high quality real-world data. Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies. Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems. This paper provides a general overview of concepts, review of recent applications, and discussion of challenges and opportunities in the context of using geosocial media data in agent-based models for urban systems. We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data, given that research moves towards standard guidelines that enable the comparison and effective use of ABMs, and geosocial media data under appropriate circumstances and applications. </p>

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Section: B Social Network Analysismentioning

confidence: 99%

Geosocial Media as a Proxy for Security: A Review

Han¹,

Li²,

Cui³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Assigning location to users on Reddit has been performed primarily by using posts in which users explicitly declare where they live (e.g. 'I live in ...') [4,12]. Hand labeling of data subsets by trained annotators has also been exercised to evaluate location inference algorithms [12].…”

Section: Location Inference Using Social Media Datamentioning

confidence: 99%

“…'I live in ...') [4,12]. Hand labeling of data subsets by trained annotators has also been exercised to evaluate location inference algorithms [12]. Our methodology overlaps with some of the approaches used in these studies such as the use of a Gazetteer (Geonames) and an out-of-the-box named entity recognition tool (Spacy), as well as a trained annotator to assess model accuracy.…”

Section: Location Inference Using Social Media Datamentioning

confidence: 99%

Location inference on social media data for agile monitoring of public health crises: An application to opioid use and abuse during the Covid-19 pandemic

Kilby¹,

Denhart²

2021

Preprint

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The Covid-19 pandemic has intersected with the opioid epidemic to create a unique public health crisis, with the health and economic consequences of the virus and associated lockdowns compounding pre-existing social and economic stressors associated with rising opioid and heroin use and abuse. In order to better understand these interlocking crises, we use social media data to extract qualitative and quantitative insights on the experiences of opioid users during the Covid-19 pandemic. In particular, we use an unsupervised learning approach to create a rich geolocated data source for public health surveillance and analysis. To do this we first infer the location of 26,000 Reddit users that participate in opiate-related subcommunities (subreddits) by combining named entity recognition, geocoding, density-based clustering, and heuristic methods. Our strategy achieves 63 percent accuracy at state-level location inference on a manually-annotated reference dataset. We then leverage the geospatial nature of our user cohort to answer policy-relevant questions about the impact of varying state-level policy approaches that balance economic versus health concerns during Covid-19. We find that state government strategies that prioritized economic reopening over curtailing the spread of the virus created a markedly different environment and outcomes for opioid users. Our results demonstrate that geospatial social media data can be used for agile monitoring of complex public health crises.

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Geocoding Without Geotags: A Text-based Approach for reddit

Cited by 2 publications

References 0 publications

Geosocial Media as a Proxy for Security: A Review

Geosocial Media as a Proxy for Security: A Review

Location inference on social media data for agile monitoring of public health crises: An application to opioid use and abuse during the Covid-19 pandemic

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