Geocoding Without Geotags: A Text-based Approach for reddit

Harrigian, Keith

doi:10.18653/v1/w18-6103

Cited by 16 publications

(19 citation statements)

References 31 publications

(37 reference statements)

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“…The Hybrid method, subsequently used in Section 4.3.2, achieves 99% test set accuracy and 68% coverage on the full dataset. Harrigian's (2018) method assigns a country estimate to every user with 78% test set accuracy. For gender, accuracy decreases from the Username, Self-reported, and Language use method, while coverage increases 11 .…”

Section: Evaluation Of Nlp Methodsmentioning

confidence: 99%

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Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

Jagfeld¹,

Lobban²,

Rayson³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine-than masculinegendered mainly young or middle-aged USbased adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues.

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Section: Evaluation Of Nlp Methodsmentioning

confidence: 99%

“…The only published method for Reddit user localisation to date (Harrigian, 2018) infers a user's country of residence via a dirichlet process mixture model 4 . It uses the distribution of words, posts per subreddit, and posts per hour of the day (timezone proxy) of a user's up to 250 most recent comments.…”

Section: Country Of Residencementioning

confidence: 99%

Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

Jagfeld¹,

Lobban²,

Rayson³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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“…Predicting latent user demographics on social media is a popular area of research, with researchers developing models to infer categories such as gender [17][18][19], age [20][21][22], race and ethnicity [23][24][25], and political orientation [26][27][28]. Specifically, latent attribute models trained on Reddit data are becoming increasingly popular, with models for inferring geolocation [29], gender [30], and mental health status [31,32], all recently published.…”

Section: Introductionmentioning

confidence: 99%

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation (Preprint)

Chew¹,

Kery²,

Baum³

et al. 2020

Preprint

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BACKGROUND Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

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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

Han

Cui

et al. 2019

IEEE Access

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Security issues such as natural disasters and terrorist attacks have attracted increasing global concern and attention. How to effectively detect security events has become worrisome to countries worldwide. Advances in mobile Internet technology have led to hundreds of millions of users using social media daily to post microblogs, text messages and multimedia information, generating enormous amounts of social media data that reflect people's social behaviors. Studies have proven that the timely intelligence can be extracted from these data. In particular, geosocial media when combined with location information can be used as a proxy for security event detection and security situational awareness. This paper provides a synopsis of the geosocial media data and the related processing/analysis methods used for detecting security events, and summarizes the general framework of security-related analyses based on geosocial media. Four major categories of analysis methods and application cases, including natural language processing, social network analysis, location inference and geospatial analysis, and image or video understanding, are discussed in detail. The paper concludes with possible future directions and areas of research that could be addressed and investigated. We hope to provide a clarion call to the scientists, practitioners, and other stakeholders to enhance the capabilities and accuracy of security events detection and security situational awareness and assessment using geosocial media.

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Geocoding Without Geotags: A Text-based Approach for reddit

Cited by 16 publications

References 31 publications

Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation (Preprint)

Geosocial Media as a Proxy for Security: A Review

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