An Empirical Study on Potential and Risks of Twitter Data for Predicting Election Outcomes

Koli, Abdul Manan; Ahmed, Muqeem; Manhas, Jatinder

doi:10.1007/978-981-13-2285-3_85

Cited by 6 publications

(7 citation statements)

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“…As result, we found that studies on Asia (73% of success) and Latin America (71%) performed better than studies on Europe (63%) and Anglo-America (54%), despite the prevalence of studies being performed on the U.S. Moreover, studies on developing economies achieved more success (74%) than on developed economies (57%), challenging the conclusions presented by Koli [34], who argued that predictions yield better results in developed countries. In terms of the approach used, the use of a volume or sentiment was not a good approach: only 55% of the 64 studies that used this approach obtained success, in contrast to 89% of the 19 studies that did not use volume or sentiment.…”

Section: Table VIII Number Of Days Between Prediction and Elections Datescontrasting

confidence: 82%

“…In 2018, Kwak [33] presented results of a survey including 69 papers which supported the argument that SM can be used in understanding political agenda, rather than in election forecast. Ultimately, most recent studies [34] [35] presented limited nonsystematic surveys, both analyzing 13 papers, adding some arguments to the original review from Gayo-Avello [30]. Koli [34] argued that prediction using Twitter can have better results in developed countries, due to a higher literacy rate and internet access, than in developing countries.…”

Section: B Analysis Of Previous Reviewsmentioning

confidence: 99%

“…Ultimately, most recent studies [34] [35] presented limited nonsystematic surveys, both analyzing 13 papers, adding some arguments to the original review from Gayo-Avello [30]. Koli [34] argued that prediction using Twitter can have better results in developed countries, due to a higher literacy rate and internet access, than in developing countries. In addition, Bilal [35] considered the challenges of sentiment analysis in languages other than English.…”

Section: B Analysis Of Previous Reviewsmentioning

confidence: 99%

“…Two of them [31] [32] just presented, in general lines, the main approaches for predicting using Twitter. The other five presented more detailed studies: Kalampokis et al [29] and Gayo-Avello [30] in 2013, Kwak and Cho [33] in 2018, Koli et al [34] in 2019, and Bilal et al [35] in 2019. These five studies were already presented on Section II and some of their results were discussed throughout the text of this study.…”

Section: A Comparison With Previous Workmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Fayaz¹

2021

IJRASET

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The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

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Section: Table VIII Number Of Days Between Prediction and Elections Datescontrasting

confidence: 82%

Section: B Analysis Of Previous Reviewsmentioning

confidence: 99%

Section: B Analysis Of Previous Reviewsmentioning

confidence: 99%

Section: A Comparison With Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Fayaz¹

2021

IJRASET

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“…Aunque la literatura actual ha demostrado que los datos de los medios sociales pueden representar en algunos casos la emoción del público y la intención de los votantes utilizando un modelo apropiado de extracción de datos [8], hay menor cantidad de estudios de caso en países en desarrollo que muestren una fusión de técnicas específicas para lograr el objetivo de predecir el resultado político en las elecciones presidenciales [9], [10].…”

Section: Introductionunclassified

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

Callejas-Cuervo

Vélez-Guerrero

2019

Rev. politec.

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La disponibilidad de los medios digitales ha proporcionado una poderosa herramienta para expresar opiniones incluyendo aspectos sociales y políticos desarrollados en cada región. En Colombia, el uso de redes sociales ha dado lugar a la difusión masiva de opiniones políticas, especialmente durante el período de campaña en las elecciones presidenciales nacionales. Este trabajo propone un modelo híbrido para predecir el desenlace de la primera vuelta en las elecciones presidenciales de Colombia en 2018 (pre-hoc), cuyo objetivo es minimizar el error absoluto y mejorar la calidad de la predicción final. Las actividades de los usuarios en Twitter y Facebook fueron registradas y analizadas, obteniendo como resultado una predicción precisa y coherente con la realidad, donde el RMSE del modelo híbrido ronda el 2,47%, superando en promedio el RMSE de las firmas encuestadoras tradicionales más prominentes del país. Adicionalmente también se predijo el valor del abstencionismo electoral con un error diferencial de 1,72% con respecto al valor real, demostrando la confiabilidad de la metodología propuesta.

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Characterization of Molecular Dynamic Trajectory Using K-means Clustering

Bijral

Manhas

Sharma

2022

Lecture Notes in Networks and Systems

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An Empirical Study on Potential and Risks of Twitter Data for Predicting Election Outcomes

Cited by 6 publications

References 10 publications

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

Characterization of Molecular Dynamic Trajectory Using K-means Clustering

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