Election Prediction on Twitter: A Systematic Mapping Study
Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a co...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5565434 |
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| _version_ | 1832568530778718208 |
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| author | Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Jianyun Shang Lin Dai Bashir Hayat |
| author_facet | Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Jianyun Shang Lin Dai Bashir Hayat |
| author_sort | Asif Khan |
| collection | DOAJ |
| description | Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions. |
| format | Article |
| id | doaj-art-de8838e2cac94a97a6edc307d616951e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-de8838e2cac94a97a6edc307d616951e2025-02-03T00:58:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55654345565434Election Prediction on Twitter: A Systematic Mapping StudyAsif Khan0Huaping Zhang1Nada Boudjellal2Arshad Ahmad3Jianyun Shang4Lin Dai5Bashir Hayat6School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of IT and Computer Science, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang Khanpur Road, Haripur 22620, PakistanSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaInstitute of Management Sciences, Peshawar 25000, PakistanContext. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.http://dx.doi.org/10.1155/2021/5565434 |
| spellingShingle | Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Jianyun Shang Lin Dai Bashir Hayat Election Prediction on Twitter: A Systematic Mapping Study Complexity |
| title | Election Prediction on Twitter: A Systematic Mapping Study |
| title_full | Election Prediction on Twitter: A Systematic Mapping Study |
| title_fullStr | Election Prediction on Twitter: A Systematic Mapping Study |
| title_full_unstemmed | Election Prediction on Twitter: A Systematic Mapping Study |
| title_short | Election Prediction on Twitter: A Systematic Mapping Study |
| title_sort | election prediction on twitter a systematic mapping study |
| url | http://dx.doi.org/10.1155/2021/5565434 |
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