A Systematic Literature Review of Machine Learning to Predict Location in Social Media
Predicting the location is a critical issue for conducting any analyses. Location analysis is one of analysis which processes Geographic Information (GI). Example in social media, users could include their posts or tweets with latitude and longitude using the Global Position System (GPS) in their d...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/278954/1/Utomo-2_TK.pdf |
| _version_ | 1797037391479832576 |
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| author | Utomo, Bayu Prasetiyo Widyawan, Widyawan Rizal, Muhammad Nur |
| author_facet | Utomo, Bayu Prasetiyo Widyawan, Widyawan Rizal, Muhammad Nur |
| author_sort | Utomo, Bayu Prasetiyo |
| collection | UGM |
| description | Predicting the location is a critical issue for conducting any analyses. Location analysis is one of analysis which processes Geographic Information (GI). Example in social media, users could include their posts or tweets with
latitude and longitude using the Global Position System (GPS) in their devices. Unfortunately, not all users or content creators provide their latitude and longitude. As a result, we investigate the literature in journals and conferences while being aware of the best methodology and algorithm employed by current researchers. Based on the predetermined inclusion and exclusion criteria, thirty-six location prediction research articles published between 2013 and 2022 were kept and selected to be investigated further. A strategy for identifying, assessing, and interpreting all study materials accessible with the goal of responding to specific questions is known as a Systematic Literature Review (SLR). The SLR result show that the topic of location prediction is still high in publication level. User text post is the most popular approach used by researchers, followed by user information and user trajectory. Almost all reviewed paper use Twitter to get the social media data. It mostly caused because of the availability throughout the Application Programming Interface (API), tool library, and datasets on the internet. Out of the twenty-six ways, four
of the most popular algorithms for predicting the location were found (RNN, LSTM, CNN and Random Forest). To raise the
performance of machine learning classifiers for position
prediction, some researchers combining any machine learning
algorithms. Therefore, the researcher suggests to using any
combined algorithm, approach, and source of data in social
media to get deeper optimization of location prediction. |
| first_indexed | 2024-03-14T00:02:37Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:278954 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:02:37Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2789542023-11-01T04:21:36Z https://repository.ugm.ac.id/278954/ A Systematic Literature Review of Machine Learning to Predict Location in Social Media Utomo, Bayu Prasetiyo Widyawan, Widyawan Rizal, Muhammad Nur Electrical and Electronic Engineering Predicting the location is a critical issue for conducting any analyses. Location analysis is one of analysis which processes Geographic Information (GI). Example in social media, users could include their posts or tweets with latitude and longitude using the Global Position System (GPS) in their devices. Unfortunately, not all users or content creators provide their latitude and longitude. As a result, we investigate the literature in journals and conferences while being aware of the best methodology and algorithm employed by current researchers. Based on the predetermined inclusion and exclusion criteria, thirty-six location prediction research articles published between 2013 and 2022 were kept and selected to be investigated further. A strategy for identifying, assessing, and interpreting all study materials accessible with the goal of responding to specific questions is known as a Systematic Literature Review (SLR). The SLR result show that the topic of location prediction is still high in publication level. User text post is the most popular approach used by researchers, followed by user information and user trajectory. Almost all reviewed paper use Twitter to get the social media data. It mostly caused because of the availability throughout the Application Programming Interface (API), tool library, and datasets on the internet. Out of the twenty-six ways, four of the most popular algorithms for predicting the location were found (RNN, LSTM, CNN and Random Forest). To raise the performance of machine learning classifiers for position prediction, some researchers combining any machine learning algorithms. Therefore, the researcher suggests to using any combined algorithm, approach, and source of data in social media to get deeper optimization of location prediction. 2022 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/278954/1/Utomo-2_TK.pdf Utomo, Bayu Prasetiyo and Widyawan, Widyawan and Rizal, Muhammad Nur (2022) A Systematic Literature Review of Machine Learning to Predict Location in Social Media. In: 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 13-14 December 2022, Yogyakarta, Indonesia. https://ieeexplore.ieee.org/document/10057871 |
| spellingShingle | Electrical and Electronic Engineering Utomo, Bayu Prasetiyo Widyawan, Widyawan Rizal, Muhammad Nur A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title | A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title_full | A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title_fullStr | A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title_full_unstemmed | A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title_short | A Systematic Literature Review of Machine Learning to Predict Location in Social Media |
| title_sort | systematic literature review of machine learning to predict location in social media |
| topic | Electrical and Electronic Engineering |
| url | https://repository.ugm.ac.id/278954/1/Utomo-2_TK.pdf |
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