How (Not) to Predict Elections
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Cites background from "How (Not) to Predict Elections"
...Critics (Metaxas et al., 2011; Gayo-Avello, 2012) have respondent that the “predictive power of Twitter regarding elections has been greatly exaggerated”: most of these electoral predictions do not perform better than mere chance....
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533 citations
Cites background from "How (Not) to Predict Elections"
...It is not rare to see potentially spurious conclusions drawn from methodologically inadequate studies [7-11], which in turn compromises the credibility of other valid studies and discourages many researchers who could benefit from adopting machine learning techniques....
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References
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"How (Not) to Predict Elections" refers background in this paper
...In fact, to describe this phenomenon, [2] talk about “predicting the future” while [3] have coined the term “predicting the present”....
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3,984 citations
"How (Not) to Predict Elections" refers background in this paper
...Empowered by the APIs that many social media companies make available, researchers are engaged in an effort to analyze and make sense of the data collected through these social communication channels....
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3,433 citations
"How (Not) to Predict Elections" refers background in this paper
...…with this formula: vote share(c1) = pos(c1) + neg(c2) pos(c1) + neg(c1) + pos(c2) + neg(c2) (1) where c1 is the candidate for whom support is being computed while c2 is the opposing candidate; pos(c) and neg(c) are, respectively, the number of positive and negative tweets mentioning candidate c....
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2,718 citations
"How (Not) to Predict Elections" refers background or methods in this paper
...Predictions were calculated based on Twitter chatter volume, as in [11], and then based on sentiment analysis of tweets, in ways similar to [10]....
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...One would expect that, following the previous research literature (e.g. [11], [12]), and given the high utilization that the Web and online social networks have in the US [1], Twitter volume should be have been able to predict consistently the outcomes of the US Congressional elections....
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...Recently, [19] provided a thorough response to the work of [11] arguing that those authors relied on a number of arbitrary choices which make their method virtually useless for future elections....
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...According to that study, the proportion of tweets mentioning each candidate should closely reflect the actual vote share in the election....
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...…with this formula: vote share(c1) = pos(c1) + neg(c2) pos(c1) + neg(c1) + pos(c2) + neg(c2) (1) where c1 is the candidate for whom support is being computed while c2 is the opposing candidate; pos(c) and neg(c) are, respectively, the number of positive and negative tweets mentioning candidate c....
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1,940 citations
"How (Not) to Predict Elections" refers background or methods or result in this paper
...” A study of a different kind was conducted by [10]....
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...It must be noted that, according to [10], the number of polarized words in the tweet is not important, and tweets can be simultaneously considered as positive and negative....
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...In [18], the sentiment analysis methods of [10] and [11] are applied to tweets obtained during the US 2008 Presidential...
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...To that end, [10] relied on the subjectivity lexicon collected by [20] and labeled tweets containing any positive word as positive tweets, and the ones containing any negative word as negative tweets....
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...While candidate counts of Twitter messages predicted with remarkable accuracy electoral results in Germany in 2009 [11], a more elaborated method did not correlate well with pre-electoral polls in the US 2008 Presidential elections [10]....
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Related Papers (5)
Frequently Asked Questions (10)
Q2. What future works have the authors mentioned in the paper "How (not) to predict elections" ?
In addition to that, further research is needed regarding the flaws of simple sentiment analysis methods when applied to political conversation. In this sense it would be very interesting to understand the impact of different lexicons and to go one step further by using machine learning techniques ( such as in the work by [ 2 ] ). Finally, the authors point out that their results do not argue against having a strategy for involving social media in a candidate ’ s election campaign.
Q3. What is the baseline for any competent predictor?
Given that, historically, the incumbent candidate gets re-elected about 9 out of 10 times, the baseline for any competent predictor should be the incumbent re-election rate.
Q4. What is the significance of the tweets?
It must be noted that, according to [10], the number of polarized words in the tweet is not important, and tweets can be simultaneously considered as positive and negative.
Q5. How many of them are used in the correlation analysis?
A little more than 14 thousand of them also appear in the MAsen10 dataset, and they are used in the following correlation analysis.
Q6. What is the sentiment score for a tweet?
Every tweet is labeled as positive, negative, or neutral, based on the sum of such labeled words (positive words contribute +1, while negative words contribute -1).
Q7. What was the effect of the second evaluation on a particular set of tweets?
2) Effect of misleading propaganda: A second evaluation was performed on a particular set of tweets, namely those included in a “Twitter bomb” targeted at Coakley [21] containing a series of tweets spreading misleading information about her.
Q8. What is the point of using the same analytical tools as one would use on data from natural phenomena?
Using on social media data the same analytical tools as one would use on data from natural phenomena may not result in repeatable predictions.
Q9. Why is there a high amount of hype surrounding the feasibility of predicting electoral results using social?
Probably due to the promising results achieved by many of the projects and studies discussed in the section I, there is a relatively high amount of hype surrounding the feasibility of predicting electoral results using social media.
Q10. What is the first method used to predict the vote share of a candidate?
The first prediction method the authors examined is the one described by [11], which consists of counting the number of tweets mentioning each candidate.