No, You Cannot Predict Elections with Twitter

Gayo-Avello, Daniel

doi:10.1109/mic.2012.137

Cited by 139 publications

(71 citation statements)

References 4 publications

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“…There are conflicting views on the reliability of social media analysis, some of the previous studies have demonstrated the correlation between election outcomes and their related posts while others, such as [8,12,32], have shown the reverse. Although their former research has been based on efficient statistical methods or/and some wellknown dictionaries as shown in Table 1, they failed.…”

Section: Related Workmentioning

confidence: 99%

“…The interpretation phase evaluates and interprets the output of the first phase by using data mining methods [12]. Its purpose is to find patterns, relevance, novelty, and interestingness [32].…”

Section: Text Miningmentioning

confidence: 99%

“…This kind of words are generally not fully exploited in most of sentimental analysis techniques [8,12,32]. However, extended words could intensify or reduce the polarity degree of the post.…”

Section: Fig 2 First Stage Of the Modelmentioning

confidence: 99%

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A novel adaptable approach for sentiment analysis on big social data

et al. 2018

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Section: Related Workmentioning

confidence: 99%

Section: Text Miningmentioning

confidence: 99%

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A novel adaptable approach for sentiment analysis on big social data

et al. 2018

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“…electoral results, product sales or stock markets developments), which have been controversially discussed [15,30,40], there have only been few attempts to examine the use of Twitter in the field of Foresight and futures research. In the following the author takes a closer look at some related studies on Twitter and Foresight.…”

Section: Twitter Researchmentioning

confidence: 99%

Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER)

Uhl¹,

Kolleck²,

Schiebel³

2016

Eur J Futures Res

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In this article the value of Twitter data analysis for a strategic foresight exercise is discussed. The article offers an overview of Twitter's basic functionalities, previous Twitter research and related studies on using Twitter in foresight projects to date. Based on this knowledge the case of the EU research project BForesight and Modelling for European Health Policy and RegulationsB(FRESHER) is used to conduct a Twitter data analysis in three steps: an analysis of weblinks to get insights into the content spread via Twitter, a social network analysis to define central actors in a Twitter debate, and a hashtag-analysis to find out which topics are discussed a n d t o su p po r t t h e i de n t i f i c at i o n o f d r i v e r s o f noncommunicable diseases. The article shows the benefit a Twitter data analysis provides for the FRESHER project and reveals implications for future research in this field.

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“…So far, the claims about the e↵ectiveness of these tools have been mixed. For instance, in the case of electoral predictions, it has been challenged in the literature on the grounds that twitter data-based methods are not an e cient, consistent predictor of electoral outcomes, at least as of now (Metaxas, Mustafaraj and Gayo-Avello, 2011;Gayo-Avello, 2012). On the other hand, applications to other areas such as financial markets have been claimed to be relatively successful.…”

Section: Prediction and Sentiment: The Role Of Feedback E↵ectsmentioning

confidence: 99%

Information aggregation and computational intelligence

Chen

Venkatachalam

2016

Evolut Inst Econ Rev

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This paper examines the possibility that the computational intelligence (CI) inspired tools can e↵ectively aggregate the rich information generated from the Web 2.0 economy, and thereby enhance the quality of decision-making. Despite many advancements and commendable applications of CI in recent years, this issue has not been well addressed. We argue that this question is intimately related to the central issue of the socialist calculation debate since the time of Friedrich Hayek. In terms of information aggregation, we examine whether there is a better engineering than the market mechanism. More precisely, we focus on whether the CI-driven sentiment analysis can generate signals like prices and whether CI can process the unstructured text data better than the market. We argue that Web 2.0 economy may not be able to set us free from information overload problems that have long co-existed with the presence of markets. We attribute this to the tacitness and subjectivity of knowledge and the recursive (feedback) characteristic of the sentiments. In this sense, Hayek's fundamental assertion that the e↵ectiveness of the market mechanism may not be so much conditioned on the information and communication technology still applies.The first author and the second author are grateful for the research support in the form of Ministry of Science and Technology (MOST) grants, MOST 103-2410-H-004-009-MY3 and MOST 104-2811-H-004-003, respectively. We thank the two anonymous referees for their valuable suggestions that helped to improve the paper. All remaining errors are our own.

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No, You Cannot Predict Elections with Twitter

Cited by 139 publications

References 4 publications

A novel adaptable approach for sentiment analysis on big social data

A novel adaptable approach for sentiment analysis on big social data

Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER)

Information aggregation and computational intelligence

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