Probabilistic topic model based approach for detecting bursty events from social media data

2017 ◽  
Author(s):  
Chunshan Li ◽  
Dianhui Chu
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Social Media Data ◽  
Model Based ◽  
Probabilistic Topic Model ◽  
Media Data

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

scholarly journals Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

10.2196/14986 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e14986 ◽  
Author(s):  
Ashlynn R Daughton ◽  
Rumi Chunara ◽  
Michael J Paul
Keyword(s):  
Infectious Disease ◽  
Social Media ◽  
Random Sample ◽  
Topic Model ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Social Media Data ◽  
Individual Level ◽  
Small Effect Size ◽  
Media Data

Background Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Results Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

scholarly journals Tracking geographical locations using a geo-aware topic model for analyzing social media data

2017 ◽  
Vol 99 ◽  
pp. 18-29 ◽  
Author(s):  
Marianela García Lozano ◽  
Jonah Schreiber ◽  
Joel Brynielsson
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Social Media Data ◽  
Media Data ◽  
Geographical Locations

scholarly journals Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach

10.2196/14763 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e14763 ◽  
Author(s):  
Jin-Young Min ◽  
Sung-Hee Song ◽  
HyeJin Kim ◽  
Kyoung-Bok Min
Keyword(s):  
Social Media ◽  
South Korea ◽  
Topic Modeling ◽  
Occupational Injury ◽  
Topic Model ◽  
Insurance Claims ◽  
Efficient Operation ◽  
Social Media Data ◽  
Hidden Knowledge ◽  
Media Data

Background Although injured employees are legally covered by workers’ compensation insurance in South Korea, some employers make agreements to prevent the injured employees from claiming their compensation. Thus, this leads to underreporting of occupational injury statistics. Illegal compensation (called gong-sang in Korean) is a critical method used to underreport or cover-up occupational injuries. However, gong-sang is not counted in the official occupational injury statistics; therefore, we cannot identify gong-sang–related issues. Objective This study aimed to analyze social media data using topic modeling to explore hidden knowledge about illegal compensation—gong-sang—for occupational injury in South Korea. Methods We collected 2210 documents from social media data by filtering the keyword, gong-sang. The study period was between January 1, 2006, and December 31, 2017. After completing natural language processing of the Korean language, a morphological analyzer, we performed topic modeling using latent Dirichlet allocation (LDA) in the Python library, Gensim. A 10-topic model was selected and run with 3000 Gibbs sampling iterations to fit the model. Results The LDA model was used to classify gong-sang–related documents into 4 categories from a total of 10 topics. Topic 1 was the greatest concern (60.5%). Workers who suffered from industrial accidents seemed to be worried about illegal compensation and legal insurance claims, wherein keywords on the choice between illegal compensation and legal insurance claims were included. In topic 2, keywords were associated with claims for industrial accident insurance benefits. Topics 3 and 4, as the second highest concern (19%), contained keywords implying the monetary compensation of gong-sang. Topics 5 to 10 included keywords on vulnerable jobs (ie, workers in the construction and defense industry, delivery riders, and foreign workers) and body parts (ie, injuries to the hands, face, teeth, lower limbs, and back) to gong-sang. Conclusions We explored hidden knowledge to identify the salient issues surrounding gong-sang using the LDA model. These topics may provide valuable information to ensure the more efficient operation of South Korea’s occupational health and safety administration and protect vulnerable workers from illegal gong-sang compensation practices.

scholarly journals Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach (Preprint)

2019 ◽  
Author(s):  
Jin-Young Min ◽  
Sung-Hee Song ◽  
HyeJin Kim ◽  
Kyoung-Bok Min
Keyword(s):  
Social Media ◽  
South Korea ◽  
Topic Modeling ◽  
Occupational Injury ◽  
Topic Model ◽  
Insurance Claims ◽  
Efficient Operation ◽  
Social Media Data ◽  
Hidden Knowledge ◽  
Media Data

BACKGROUND Although injured employees are legally covered by workers’ compensation insurance in South Korea, some employers make agreements to prevent the injured employees from claiming their compensation. Thus, this leads to underreporting of occupational injury statistics. Illegal compensation (called <italic>gong-sang</italic> in Korean) is a critical method used to underreport or cover-up occupational injuries. However, <italic>gong-sang</italic> is not counted in the official occupational injury statistics; therefore, we cannot identify <italic>gong-sang</italic>–related issues. OBJECTIVE This study aimed to analyze social media data using topic modeling to explore hidden knowledge about illegal compensation—<italic>gong-sang</italic>—for occupational injury in South Korea. METHODS We collected 2210 documents from social media data by filtering the keyword, <italic>gong-sang</italic>. The study period was between January 1, 2006, and December 31, 2017. After completing natural language processing of the Korean language, a morphological analyzer, we performed topic modeling using latent Dirichlet allocation (LDA) in the Python library, Gensim. A 10-topic model was selected and run with 3000 Gibbs sampling iterations to fit the model. RESULTS The LDA model was used to classify <italic>gong-sang</italic>–related documents into 4 categories from a total of 10 topics. Topic 1 was the greatest concern (60.5%). Workers who suffered from industrial accidents seemed to be worried about illegal compensation and legal insurance claims, wherein keywords on the choice between illegal compensation and legal insurance claims were included. In topic 2, keywords were associated with claims for industrial accident insurance benefits. Topics 3 and 4, as the second highest concern (19%), contained keywords implying the monetary compensation of <italic>gong-sang</italic>. Topics 5 to 10 included keywords on vulnerable jobs (ie, workers in the construction and defense industry, delivery riders, and foreign workers) and body parts (ie, injuries to the hands, face, teeth, lower limbs, and back) to <italic>gong-sang</italic>. CONCLUSIONS We explored hidden knowledge to identify the salient issues surrounding <italic>gong-sang</italic> using the LDA model. These topics may provide valuable information to ensure the more efficient operation of South Korea’s occupational health and safety administration and protect vulnerable workers from illegal <italic>gong-sang</italic> compensation practices.

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