Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

A comprehensive analysis of forecasting elections using social media text

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Modern social media’s rise to prominence has altered the ways in which candidates reach out to voters and conduct campaigns. Researchers often dwell upon the uses of social media platforms as a plethora of information for various tasks, such as election prediction, since they contain a large volume of people’s ideas about politics and leaders. Modern political campaigns and party propaganda make extensive use of social media. It is common practise for political parties and candidates to utilise Twitter and other social media during election season for coverage and promotion. This study analyses and provides estimates for the reliability of several volumetric social media techniques to predict election outcomes from social media activity. Incredibly large datasets made available by social media sites may be mined for insights into societal problems and predictions about the future. However, this is difficult because of the skewed and noisy nature of the data. This literature review aims to enlighten readers about the researchers’ input towards the process of forecasting election outcomes using social media content by outlining an assessment of sentiment analysis and its methodologies. The study also discusses research that aims to foretell upcoming elections in several nations by analysing user textual data on social media sites. In addition, this paper has pointed out some of the research gaps that exist in the area of election outcome forecasting and some of the challenging questions in the domain of sentiment analysis. In addition, this paper makes recommendations for the future of election prediction based on material gleaned from social media.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Jungherr A (2016) Twitter use in election campaigns: A systematic literature review. J Inf Techn Politics 13(1):72–91

    Article  Google Scholar 

  2. Smith TW (1990) The first straw? a study of the origins of election polls. Public Opinion Quarterly 54(1):21–36

    Article  MATH  Google Scholar 

  3. Mehrabian L (1998) Effects of poll reports on voter preferences. J Appl Soc Psychol 28(23):2119–2130

    Article  MATH  Google Scholar 

  4. Campbell JE (1992) Forecasting the presidential vote in the states. American J Political Sci 36(2):386–407. Accessed 11 Dec 2022

  5. Lewis-Beck MS, Rice TW (1984) Forecasting presidential elections: A comparison of naive models. Political Behavior 6(1):9–21

    Article  MATH  Google Scholar 

  6. Lewis-Beck MS (2005) Election forecasting: Principles and practice. British J Polit Int Relat 7(2):145–164

    Article  MATH  Google Scholar 

  7. Rothschild D (2015) Combining forecasts for elections: Accurate, relevant, and timely. Int J Forecast 31(3):952–964

    Article  MATH  Google Scholar 

  8. Vergeer M (2013) Politics, elections and online campaigning: Past, present... and a peek into the future. New media & society 15(1):9–17

  9. Williams LV, Reade JJ (2016) Forecasting elections. J Forecast 35(4):308–328

    Article  MathSciNet  MATH  Google Scholar 

  10. Linzer D, Lewis-Beck MS (2015) Forecasting us presidential elections: New approaches (an introduction). Int J Forecast 31(3):895–897

    Article  MATH  Google Scholar 

  11. MacDonald R, Mao X (2016) Forecasting the 2015 General Election with Internet Big Data: An Application of the TRUST Framework. University of Glasgow, Adam Smith Business School, Glasgow, UK

    MATH  Google Scholar 

  12. Burnap P, Gibson R, Sloan L, Southern R, Williams M (2016) 140 characters to victory?: Using twitter to predict the uk 2015 general election. Electoral Studies 41:230–233

    Article  Google Scholar 

  13. Ibrahim M, Abdillah O, Wicaksono AF, Adriani M (2015) Buzzer detection and sentiment analysis for predicting presidential election results in a twitter nation. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp 1348–1353. IEEE

  14. Garcia ACB, Silva W, Correia L (2018) The prednews forecasting model. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, pp 1–6

  15. Newman N, Fletcher R, Schulz A, Andi S, Robertson CT, Nielsen RK (2021) Reuters institute digital news report 2021. Reuters Institute for the study of Journalism

  16. Mavragani A, Tsagarakis KP (2016) Yes or no: Predicting the 2015 greferendum results using google trends. Technol Forecast Soc Chang 109:1–5

    Article  Google Scholar 

  17. Naseem R, Shaukat Z, Irfan M, Shah MA, Ahmad A, Muhammad F, Glowacz A, Dunai L, Antonino-Daviu J, Sulaiman A (2021) Empirical assessment of machine learning techniques for software requirements risk prediction. Electronics 10(2):168

    Article  Google Scholar 

  18. Ullah A, Wang J, Anwar MS, Ahmad A, Nazir S, Khan HU, Fei Z (2021) Fusion of machine learning and privacy preserving for secure facial expression recognition. Security and Communication Networks 2021

  19. Boudjellal N, Zhang H, Khan A, Ahmad A, Naseem R, Shang J, Dai L (2021) Abioner: a bert-based model for arabic biomedical named-entity recognition. Complexity 2021

  20. Khan A, Zhang H, Shang J, Boudjellal N, Ahmad A, Ali A, Dai L (2020) Predicting politician’s supporters’ network on twitter using social network analysis and semantic analysis. Scientific Programming 2020

  21. Tilton S (2008) Virtual polling data: A social network analysis on a student government election. Webology 5(4):1–8

    MATH  Google Scholar 

  22. Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 4, pp 178–185

  23. O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: Linking text sentiment to public opinion time series. In: Fourth International AAAI Conference on Weblogs and Social Media

  24. Sang ETK, Bos J (2012) Predicting the 2011 dutch senate election results with twitter. In: Proceedings of the Workshop on Semantic Analysis in Social Media, pp 53–60

  25. Ceron A, Curini L, Iacus SM, Porro G (2014) Every tweet counts? how sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to italy and france. New media & society 16(2):340–358

    Article  MATH  Google Scholar 

  26. Cerón-Guzmán JA, León-Guzmán E (2016) A sentiment analysis system of spanish tweets and its application in colombia 2014 presidential election. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (socialcom), Sustainable Computing and Communications (sustaincom)(BDCloud-socialcom-sustaincom), pp 250–257. IEEE

  27. Dwi Prasetyo N, Hauff C (2015) Twitter-based election prediction in the developing world. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp 149–158

  28. Rodríguez S, Allende-Cid H, Palma W, Alfaro R, Gonzalez C, Elortegui C, Santander P (2018) Forecasting the chilean electoral year: Using twitter to predict the presidential elections of 2017. In: International Conference on Social Computing and Social Media, pp 298–314. Springer

  29. Heredia B, Prusa JD, Khoshgoftaar TM (2018) Social media for polling and predicting united states election outcome. Soc Netw Anal Min 8(1):1–16

    Article  MATH  Google Scholar 

  30. Kalampokis E, Tambouris E, Tarabanis K (2013) Understanding the predictive power of social media. Internet Research

  31. Gayo-Avello D (2013) A meta-analysis of state-of-the-art electoral prediction from twitter data. Soc Sci Comp Rev 31(6):649–679

    Article  MATH  Google Scholar 

  32. Prada J (2015) Predicting with twitter. In: Proceedings of the 2nd European Conference on Social Media

  33. O’Leary DE (2015) Twitter mining for discovery, prediction and causality: Applications and methodologies. Intell Syst Acc Finance Manag 22(3):227–247

    Article  MATH  Google Scholar 

  34. Kwak J-A, Cho SK (2018) Analyzing public opinion with social media data during election periods: A selective literature review. Asian J Public Opin Res 5(4):285–301

    MATH  Google Scholar 

  35. Koli AM, Ahmed M, Manhas J (2019) An empirical study on potential and risks of twitter data for predicting election outcomes. In: Emerging Trends in Expert Applications and Security, pp 725–731. Springer, Jaipur, India

  36. Bilal M, Gani A, Marjani M, Malik N (2019) Predicting elections: Social media data and techniques. In: 2019 International Conference on Engineering and Emerging Technologies (ICEET), pp 1–6. IEEE

  37. Chauhan P, Sharma N, Sikka G (2021) The emergence of social media data and sentiment analysis in election prediction. J Ambient Intell Humanized Comput 12(2):2601–2627

    Article  MATH  Google Scholar 

  38. Singh P, Sawhney RS (2018) Influence of twitter on prediction of election results. In: Progress in Advanced Computing and Intelligent Engineering, pp 665–673. Springer, India

  39. Social WA (2020) Hootsuite.(2020). Indonesia Digital report

  40. Liu X, Ren F, Su G, Zhang M, Gu W, Kato S (2024) Predicting voting outcomes for multi-alternative elections in social networks. IEEE Access

  41. Feng G, Chen K, Cai H, Li Z (2023) A hybrid method of sentiment analysis and machine learning algorithm for the us presidential election forecasting. In: 2023 IEEE International Conference on Big Data (BigData), pp 1495–1500. IEEE

  42. Joseph A, George S, Samuel RK (2022) A p2p digital voting system for elections in india. In: 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), pp 1–5. IEEE

  43. Bayrak C, Kutlu M (2022) Predicting election results via social media: A case study for 2018 turkish presidential election. IEEE Transactions on Computational Social Systems

  44. Fagni T, Cresci S (2022) Fine-grained prediction of political leaning on social media with unsupervised deep learning. J Artif Intell Res 73:633–672

    Article  MATH  Google Scholar 

  45. Kumar A, Sebastian TM et al (2012) Sentiment analysis: A perspective on its past, present and future. Int J Intell Syst Appl 4(10):1–14

    MATH  Google Scholar 

  46. Vinodhini G, Chandrasekaran R (2012) Sentiment analysis and opinion mining: a survey. Int J 2(6):282–292

    MATH  Google Scholar 

  47. Habimana O, Li Y, Li R, Gu X, Yu G (2020) Sentiment analysis using deep learning approaches: an overview. Sci China Inf Sci 63(1):1–36

    Article  MATH  Google Scholar 

  48. Agarwal B, Mittal N (2016) Prominent feature extraction for review analysis: an empirical study. J Exp Theor Artif Intell 28(3):485–498

    Article  MATH  Google Scholar 

  49. Giachanou A, Crestani F (2016) Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49(2):1–41

    Article  MATH  Google Scholar 

  50. Singh NK, Tomar DS, Sangaiah AK (2020) Sentiment analysis: a review and comparative analysis over social media. J Ambient Intell Humanized Comput 11(1):97–117

    Article  MATH  Google Scholar 

  51. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: A survey. Ain Shams Eng J 5(4):1093–1113

    Article  MATH  Google Scholar 

  52. Tedmori S, Awajan A (2019) Sentiment analysis main tasks and applications: a survey. J Inf Process Syst 15(3):500–519

    Google Scholar 

  53. Siqueira H, Barros F (2010) A feature extraction process for sentiment analysis of opinions on services. In: Proceedings of International Workshop on Web and Text Intelligence, pp 404–413

  54. Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Expert Syst Appl 57:117–126

    Article  MATH  Google Scholar 

  55. Vateekul P, Koomsubha T (2016) A study of sentiment analysis using deep learning techniques on thai twitter data. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp 1–6. IEEE

  56. Çano E, Morisio M (2018) A deep learning architecture for sentiment analysis. In: Proceedings of the International Conference on Geoinformatics and Data Analysis, pp 122–126

  57. Sun B, Tian F, Liang L (2018) Tibetan micro-blog sentiment analysis based on mixed deep learning. In: 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp 109–112. IEEE

  58. Rani S, Kumar P (2019) Deep learning based sentiment analysis using convolution neural network. Arabian J Sci Eng 44(4):3305–3314

    Article  MATH  Google Scholar 

  59. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  60. Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4):1253

    MATH  Google Scholar 

  61. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp 1188–1196. PMLR

  62. Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532–1543

  63. Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146

    Article  Google Scholar 

  64. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. CoRR arxiv:1802.05365

  65. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  66. Priyavrat A (2017) Sentiment analysis: A comparative study of supervised machine learning algorithms using rapid miner. International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN, 2321–9653

  67. Mohammad SM, Kiritchenko S, Zhu X (2013) Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242

  68. Hemmatian F, Sohrabi MK (2019) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 52(3):1495–1545

    Article  MATH  Google Scholar 

  69. Ay Karakuş B, Talo M, Hallaç İR, Aydin G (2018) Evaluating deep learning models for sentiment classification. Concurrency and Computation: Practice and Experience 30(21):4783

    Article  MATH  Google Scholar 

  70. Sharma N et al (2018) Sentiment analysis using tidytext package in r. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), pp 577–580. IEEE

  71. Rosas VP, Mihalcea R, Morency L-P (2013) Multimodal sentiment analysis of spanish online videos. IEEE Intell Syst 28(3):38–45

    Article  MATH  Google Scholar 

  72. Mumtaz D, Ahuja B (2018) A lexical and machine learning-based hybrid system for sentiment analysis. In: Innovations in Computational Intelligence, pp 165–175. Springer, Singapore

  73. Srinivasarao U, Sharaff A (2023) Sms sentiment classification using an evolutionary optimization based fuzzy recurrent neural network. Multimed Tools Appl 82(27):42207–42238

    Article  MATH  Google Scholar 

  74. Srinivasarao U, Sharaff A (2023) Machine intelligence based hybrid classifier for spam detection and sentiment analysis of sms messages. Multimed Tools Appl 82(20):31069–31099

    Article  MATH  Google Scholar 

  75. Dissemination C (2009) Systematic reviews: Crd’s guidance for undertaking reviews in healthcare. University of York NHS Centre for Reviews & Dissemination, York

    Google Scholar 

  76. Chen E, Deb A, Ferrara E (2022) # election2020: the first public twitter dataset on the 2020 us presidential election. J Comput Soc Sci 1–18

  77. Bhadauria AS, Shukla M, Kumar P, Dwivedi N (2024) Forecasting election sentiments: Deep learning vs. traditional models. In: 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp 1–6. IEEE

  78. Sabuncu I (2020) USA Nov.2020 Election 20 Mil. Tweets (with Sentiment and Party Name Labels) Dataset. https://doi.org/10.21227/25te-j338

  79. Khare A, Gangwar A, Singh S, Prakash S (2023) Sentiment analysis and sarcasm detection in indian general election tweets. In: Research Advances in Intelligent Computing, pp 253–268. CRC Press, ???

  80. Lai M, Cignarella AT, Farías DIH, Bosco C, Patti V, Rosso P (2020) Multilingual stance detection in social media political debates. Computer Speech & Language 63:101075

    Article  Google Scholar 

  81. König T, Schünemann WJ, Brand A, Freyberg J, Gertz M (2022) The epinetz twitter politicians dataset 2021. a new resource for the study of the german twittersphere and its application for the 2021 federal elections. Politische Vierteljahresschrift 63(3):529–547

    Article  Google Scholar 

  82. Yaqub U, Sharma N, Pabreja R, Chun SA, Atluri V, Vaidya J (2020) Location-based sentiment analyses and visualization of twitter election data. Digital Government: Research and Practice 1(2):1–19

    Article  Google Scholar 

  83. Kausar S, Tahir B, Mehmood MA (2021) Towards understanding trends manipulation in pakistan twitter. arXiv preprint arXiv:2109.14872

  84. Pierri F, Liu G, Ceri S (2023) Ita-election-2022: A multi-platform dataset of social media conversations around the 2022 italian general election. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp 5386–5390

  85. Skoric M, Poor N, Achananuparp P, Lim E-P, Jiang J (2012) Tweets and votes: A study of the 2011 singapore general election. In: 2012 45th Hawaii International Conference on System Sciences, pp 2583–2591. IEEE

  86. Bermingham A, Smeaton A (2011) On using twitter to monitor political sentiment and predict election results. In: Proceedings of the Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP 2011), pp 2–10

  87. Gayo-Avello D (2011) Don’t turn social media into another’literary digest’poll. Communications of the ACM 54(10):121–128

    Article  Google Scholar 

  88. Boutet A, Kim H, Yoneki E (2012) What’s in your tweets? i know who you supported in the uk 2010 general election. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 6, pp 411–414

  89. Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp 115–120

  90. Shi L, Agarwal N, Agrawal A, Garg R, Spoelstra J (2012) Predicting us primary elections with twitter 4. http://snap.stanford.edu/social2012/papers/shi.pdf

  91. Ahmed S, Skoric MM (2014) My name is khan: the use of twitter in the campaign for 2013 pakistan general election. In: 2014 47th Hawaii International Conference on System Sciences, pp 2242–2251. IEEE

  92. Song M, Kim MC, Jeong YK (2014) Analyzing the political landscape of 2012 korean presidential election in twitter. IEEE Intell Syst 29(2):18–26

    Article  MATH  Google Scholar 

  93. Khatua A, Khatua A, Ghosh K, Chaki N (2015) Can# twitter_trends predict election results? evidence from 2014 indian general election. In: 2015 48th Hawaii International Conference on System Sciences, pp 1676–1685. IEEE

  94. Tsakalidis A, Papadopoulos S, Cristea AI, Kompatsiaris Y (2015) Predicting elections for multiple countries using twitter and polls. IEEE Intell Syst 30(2):10–17

    Article  MATH  Google Scholar 

  95. Jaidka K, Ahmed S, Skoric M, Hilbert M (2019) Predicting elections from social media: a three-country, three-method comparative study. Asian J Commun 29(3):252–273

    Article  Google Scholar 

  96. Xie Z, Liu G, Wu J, Tan Y (2018) Big data would not lie: prediction of the 2016 taiwan election via online heterogeneous information. EPJ Data Sci 7(1):1–16

    Article  MATH  Google Scholar 

  97. Livne A, Simmons M, Adar E, Adamic L (2011) The party is over here: Structure and content in the 2010 election. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 5, pp 201–208

  98. Chung JE, Mustafaraj E (2011) Can collective sentiment expressed on twitter predict political elections? In: Twenty-fifth AAAI Conference on Artificial Intelligence

  99. Anjaria M, Guddeti RMR (2014) A novel sentiment analysis of social networks using supervised learning. Soc Network Anal Min 4(1):1–15

    MATH  Google Scholar 

  100. Ceron A, Curini L, Iacus SM (2015) Using sentiment analysis to monitor electoral campaigns: Method matters—evidence from the united states and italy. Soc Sci Comput Rev 33(1):3–20

    Article  Google Scholar 

  101. Singhal K, Agrawal B, Mittal N (2015) Modeling indian general elections: sentiment analysis of political twitter data. In: Information Systems Design and Intelligent Applications, pp 469–477. Springer, Proceedings of Second International Conference INDIA 2015, Volume 1

  102. You Q, Cao L, Cong Y, Zhang X, Luo J (2015) A multifaceted approach to social multimedia-based prediction of elections. IEEE Trans Multimed 17(12):2271–2280

    Article  MATH  Google Scholar 

  103. Sharma P, Moh T-S (2016) Prediction of indian election using sentiment analysis on hindi twitter. In: 2016 IEEE International Conference on Big Data (big Data), pp 1966–1971. IEEE

  104. Elghazaly T, Mahmoud A, Hefny HA (2016) Political sentiment analysis using twitter data. In: Proceedings of the International Conference on Internet of Things and Cloud Computing, pp 1–5

  105. Jose R, Chooralil VS (2016) Prediction of election result by enhanced sentiment analysis on twitter data using classifier ensemble approach. In: 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pp 64–67. IEEE

  106. Awais M, Hassan S-U, Ahmed A (2021) Leveraging big data for politics: predicting general election of pakistan using a novel rigged model. J Ambient Intell Humanized Comput 12(4):4305–4313

    Article  MATH  Google Scholar 

  107. Chandra R, Saini R (2021) Biden vs trump: modeling us general elections using bert language model. IEEE Access 9:128494–128505

    Article  Google Scholar 

  108. Gohil R, Deepa S, Vinay M, Jayapriya J (2023) Election forecasting with machine learning and sentiment analysis: Karnataka 2023. In: 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), pp 1–6. IEEE

  109. Gayo-Avello D, Metaxas P, Mustafaraj E (2011) Limits of electoral predictions using twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 5, pp 490–493

  110. Choy M, Cheong ML, Laik MN, Shung KP (2011) A sentiment analysis of singapore presidential election 2011 using twitter data with census correction. arXiv preprint arXiv:1108.5520

  111. Jungherr A, Jürgens P, Schoen H (2012) Why the pirate party won the german election of 2009 or the trouble with predictions: A response to tumasjan, a., sprenger, to, sander, pg, & welpe, im “predicting elections with twitter: What 140 characters reveal about political sentiment”. Social science computer review 30(2):229–234

  112. Dang-Xuan L, Stieglitz S, Wladarsch J, Neuberger C (2013) An investigation of influentials and the role of sentiment in political communication on twitter during election periods. Inf Commun Soc 16(5):795–825

    Article  Google Scholar 

  113. Unankard S, Li X, Sharaf M, Zhong J, Li X (2014) Predicting elections from social networks based on sub-event detection and sentiment analysis. In: International Conference on Web Information Systems Engineering, pp 1–16. Springer

  114. Jose R, Chooralil VS (2015) Prediction of election result by enhanced sentiment analysis on twitter data using word sense disambiguation. In: 2015 International Conference on Control Communication & Computing India (ICCC), pp 638–641. IEEE

  115. Singh P, Sawhney RS, Kahlon KS (2017) Forecasting the 2016 us presidential elections using sentiment analysis. In: Conference on e-Business, e-Services and e-Society, pp 412–423. Springer

  116. Wang L, Gan JQ (2017) Prediction of the 2017 french election based on twitter data analysis. In: 2017 9th Computer Science and Electronic Engineering (CEEC), pp 89–93. IEEE

  117. Tsakalidis A, Aletras N, Cristea AI, Liakata M (2018) Nowcasting the stance of social media users in a sudden vote: The case of the greek referendum. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 367–376

  118. Budiharto W, Meiliana M (2018) Prediction and analysis of indonesia presidential election from twitter using sentiment analysis. J Big data 5(1):1–10

    Article  Google Scholar 

  119. Brito K, Paula N, Fernandes M, Meira S (2019) Social media and presidential campaigns–preliminary results of the 2018 brazilian presidential election. In: Proceedings of the 20th Annual International Conference on Digital Government Research, pp 332–341

  120. Bose R, Dey RK, Roy S, Sarddar D (2019) Analyzing political sentiment using twitter data. In: Information and Communication Technology for Intelligent Systems, pp 427–436. Springer, Ahmedabad, India

  121. Kristiyanti DA, Umam AH et al (2019) Prediction of indonesia presidential election results for the 2019-2024 period using twitter sentiment analysis. In: 2019 5th International Conference on New Media Studies (CONMEDIA), pp 36–42. IEEE

  122. Batra PK, Saxena A, Goel C et al (2020) Election result prediction using twitter sentiments analysis. In: 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp 182–185. IEEE

  123. Hadi MA, Fard FH, Vrbik I (2020) Geo-spatial data visualization and critical metrics predictions for canadian elections. In: 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp 1–7. IEEE

  124. Jhawar A, Munjal V, Ranjan S, Karmakar P (2020) Social network based sentiment and network analysis to predict elections. In: 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp 1–6. IEEE

  125. Padwal A, Koshy R (2021) A hybrid approach to predict election candidate success using candidate speech and voter opinion. In: 2021 International Conference on Communication Information and Computing Technology (ICCICT), pp 1–4. IEEE

  126. Das A, Gunturi KS, Chandrasekhar A, Padhi A, Liu Q (2021) Automated pipeline for sentiment analysis of political tweets. In: 2021 International Conference on Data Mining Workshops (ICDMW), pp 128–135. IEEE

  127. Dhanya M, Megha M, Kannath M, Al Mansoori S, Panthakkan A (2021) Explorative & predictive analysis of covid-19 in us and its impact on us presidential election. In: 2021 4th International Conference on Signal Processing and Information Security (ICSPIS), pp 61–64. IEEE

  128. Okimoto Y, Hosokawa Y, Zhang J, Li L (2021) Japanese election prediction based on sentiment analysis of twitter replies to candidates. In: 2021 International Conference on Asian Language Processing (IALP), pp 322–327. IEEE

  129. Fachrie M, Ardiani F (2021) Predictive model for regional elections results based on candidate profiles. In: 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp 247–252. IEEE

  130. Da Silva TP, Parmezan AR, Batista GE (2021) A graph-based spatial cross-validation approach for assessing models learned with selected features to understand election results. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 909–915. IEEE

  131. Nugroho DK (2021) Us presidential election 2020 prediction based on twitter data using lexicon-based sentiment analysis. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp 136–141. IEEE

  132. Perera S, Karunanayaka K (2022) Sentiment analysis of social media data using fuzzy-rough set classifier for the prediction of the presidential election. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC), pp 188–193. IEEE

  133. Ali H, Farman H, Yar H, Khan Z, Habib S, Ammar A (2022) Deep learning-based election results prediction using twitter activity. Soft Computing 26(16):7535–7543

    Article  MATH  Google Scholar 

  134. Xie Z, Liu G, Wu J, Tan Y (2018) Supplementary information for big data would not lie: prediction of the 2016 Taiwan election via online heterogeneous information. EPJ Data Sci 7(1):1–16.

  135. Stieglitz S, Dang-Xuan L (2013) Social media and political communication: a social media analytics framework. Social Netw Anal Min 3:1277–1291

    Article  MATH  Google Scholar 

  136. Shorey S, Howard P (2016) Automation, big data and politics: A research review. International Journal of Communication 10

  137. Anstead N, O’Loughlin B (2015) Social media analysis and public opinion: The 2010 uk general election. J Computer-mediated Commun 20(2):204–220

    Article  MATH  Google Scholar 

  138. Bovet A, Morone F, Makse HA (2018) Validation of twitter opinion trends with national polling aggregates: Hillary clinton vs donald trump. Sci Rep 8(1):8673

    Article  Google Scholar 

  139. Bennett WL (2012) The personalization of politics: Political identity, social media, and changing patterns of participation. The annals of the American academy of political and social science 644(1):20–39

    Article  MATH  Google Scholar 

  140. Mellon J, Prosser C (2017) Twitter and facebook are not representative of the general population: Political attitudes and demographics of british social media users. Res Polit 4(3):2053168017720008

    Article  Google Scholar 

  141. Brito KDS, Silva Filho RLC, Adeodato PJL (2021) A systematic review of predicting elections based on social media data: research challenges and future directions. IEEE Trans Comput Soc Syst 8(4):819–843

  142. Shahzad F (2021) Uses of artificial intelligence and big data for election campaign in turkey. Master’s thesis, Marmara Universitesi (Turkey)

  143. Crane H, Martin R (2017) Rethinking probabilistic prediction in the wake of the 2016 us presidential election. arXiv preprint arXiv:1704.01171

  144. Rothschild D (2009) Forecasting elections: Comparing prediction markets, polls, and their biases. Public Opinion Quarterly 73(5):895–916

    Article  MATH  Google Scholar 

  145. Seib P (1994) Campaigns and Conscience: The Ethics of Political Journalism. Bloomsbury Publishing, USA

    MATH  Google Scholar 

  146. Jens CE (2017) Political uncertainty and investment: Causal evidence from us gubernatorial elections. J Financ Econ 124(3):563–579

    Article  MATH  Google Scholar 

  147. Hashim N, El Mosallamy D (2020) Presidential elections and stock market: a comparative study. J Financ Econ 8(3):116–126

    MATH  Google Scholar 

  148. Hussain H, Al Tajir K, Habib R, Abboud S, Fadel S (2023) The effects of political polarization on financial decision making. Fusion of Multidisciplinary Research, An International Journal 4(1):420–431

    MATH  Google Scholar 

  149. Nguyen TT, Hatua A, Sung AH (2023) How to detect ai-generated texts? In: 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp 0464–0471. IEEE

  150. Wang T-L (2020) Does fake news matter to election outcomes?: The case study of taiwan’s 2018 local elections. Asian J Public Opin Res 8(2):67–104

    MATH  Google Scholar 

  151. Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D et al (2018) The science of fake news. Science 359(6380):1094–1096

    Article  Google Scholar 

  152. Lyon D (2014) Surveillance, snowden, and big data: Capacities, consequences, critique. Big data & society 1(2):2053951714541861

    Article  MATH  Google Scholar 

  153. Richards NM, King JH (2014) Big data ethics. Wake Forest L Rev 49:393

    MATH  Google Scholar 

Download references

Funding

There is no funding associated with this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abhishek Gaur.

Ethics declarations

Conflicts of interests

There are no competing interests among the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gaur, A., Yadav, D.K. A comprehensive analysis of forecasting elections using social media text. Multimed Tools Appl (2025). https://doi.org/10.1007/s11042-024-20528-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-20528-w

Keywords