A Location Inferring Model Based on Tweets and Bilateral Follow Friends

Wang, Xia; Xu, Ming; Ren, Yi; Xu, Jian; Zhang, Haiping; Zheng, Ning

doi:10.4304/jcp.9.2.315-321

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current online-corpus location inferring algorithms mainly considers the text associated with each user, including the gazetteer-based method [ 37 ], part-of-speech tagging [ 38 , 39 ] and named entity recognition (NER) [ 40 , 41 ]. The summary and comparison of the three algorithms are presented in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Location inference for hidden population with online text analysis

Liu

Cao

2020

Int J Health Geogr

View full text Add to dashboard Cite

Background Understanding the geographic distribution of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequate deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level. Methods We collect a comprehensive dataset from the largest sub-community related to MSM topics in Baidu Tieba, covering 628,360 MSM-related users. Based on users’ publicly available posts, we evaluate and compare the performances of mainstream location inference algorithms on the online locating problem of Chinese MSM population. To improve the inference accuracy, other approaches in natural language processing are introduced into the location extraction, such as context analysis and pattern recognition. In addition, we develop a hybrid voting algorithm (HVA-LI) by allowing different approaches to vote to determine the best inference results, which guarantees a more effective way on location inference for hidden population. Results By comparing the performances of popular inference algorithms, we find that the classic gazetteer-based algorithm has achieved better results. And in the HVA-LI algorithms, the hybrid algorithm consisting of the simple gazetteer-based method and named entity recognition (NER) is proven to be the best to deal with inferring users’ locations disclosed in short texts on online communities, improving the inferring accuracy from 50.3 to 71.3% on the MSM-related dataset. Conclusions In this study, we have explored the possibility of location inferring by analyzing textual content posted by online users. A more effective hybrid algorithm, i.e., the Gazetteer & NER algorithm is proposed, which is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.

show abstract

Section: Methodsmentioning

confidence: 99%

Location inference for hidden population with online text analysis

Liu

Cao

2020

Int J Health Geogr

View full text Add to dashboard Cite

show abstract

“…A majority of previous works either focus on a local region e.g. United States [5], Sweden [2], or using rich user information like a certain number of tweets for each user [5], user's social relationship [12,19,27]. Different from these works, this paper works on worldwide tweet location prediction.…”

Section: Related Workmentioning

confidence: 99%

On Predicting Geolocation of Tweets Using Convolutional Neural Networks

Huang¹,

Carley²

2017

Social, Cultural, and Behavioral Modeling

View full text Add to dashboard Cite

In many Twitter studies, it is important to know where a tweet came from in order to use the tweet content to study regional user behavior. However, researchers using Twitter to understand user behavior often lack sufficient geo-tagged data. Given the huge volume of Twitter data there is a need for accurate automated geolocating solutions. Herein, we present a new method to predict a Twitter user's location based on the information in a single tweet. We integrate text and user profile meta-data into a single model using a convolutional neural network. Our experiments demonstrate that our neural model substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1% accuracy on city-level and country-level prediction respectively. 1 https://dev.twitter.com/docs

show abstract

Recent research trends for geospatial information explored by Twitter data

Kim

Koh

2016

Spat. Inf. Res.

View full text Add to dashboard Cite

A Location Inferring Model Based on Tweets and Bilateral Follow Friends

Cited by 4 publications

References 21 publications

Location inference for hidden population with online text analysis

Location inference for hidden population with online text analysis

On Predicting Geolocation of Tweets Using Convolutional Neural Networks

Recent research trends for geospatial information explored by Twitter data

Contact Info

Product

Resources

About