Twitter, Public Opinion, and the 2011 Nigerian Presidential Election

Fink, Clay; Bos, Nathan; Perrone, Alexander; Liu, Edwina; Kopecky, Jonathon

doi:10.1109/socialcom.2013.50

Cited by 25 publications

(29 citation statements)

References 12 publications

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“…Bermingham and Smeaton 2011;Sanders and van den Bosch 2013;Skoric et al 2012;Soler et al 2012;Tjong et al 2012;Tumasjan et al 2010), the mention share of political candidates (e.g. Chen et al 2012;DiGrazia et al 2013;Fink et al 2013;Gaurav et al 2013;Skoric et al 2012), the share of positive mentions a party received (e.g. Bermingham and Smeaton 2011;Thapen and Ghanem 2013), the positive mention share of candidates (e.g.…”

Section: Predicting Election Results Using Twitter Data: the Evidencementioning

confidence: 99%

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Analyzing Political Communication with Digital Trace Data

Jungherr

2015

Contributions to Political Science

104

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Section: Predicting Election Results Using Twitter Data: the Evidencementioning

confidence: 99%

“…Bermingham and Smeaton 2011;Thapen and Ghanem 2013), the positive mention share of candidates (e.g. Fink et al 2013;O'Connor et al 2010;Shi et al 2012), the share data in their relationship to various metrics of electoral success (e.g. Diaz et al 2014;Huberty 2013;Jungherr et al 2012).…”

Section: Predicting Election Results Using Twitter Data: the Evidencementioning

confidence: 99%

Analyzing Political Communication with Digital Trace Data

Jungherr

2015

Contributions to Political Science

104

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“…Social media are used to detect various events, such as earthquakes [2-4], political elections [5-7], and stock prices in a market [8]. Among the various applications, the study on health-related event detection referred to as infodemiology [1,34] has been gaining much attention from researchers in areas such as air pollution [49], Web-based doctor reviews [50], West Nile virus [9], cholera [10], Escherichia coli outbreak [11], dengue fever outbreak [12], and influenza [1,13-33].…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, various approaches for analyzing social data (called as social monitoring [1]) have been presented so far. These approaches have presented an important shared premise that Twitter users can be human sensors for event detection [2], and the feasibility of these approaches has been demonstrated on various occasions such as earthquakes [2-4], political elections [5-7], stock market fluctuations [8], and outbreaks of various infectious diseases [9-33]. Among them, the study of social monitoring of health-related information shared on the internet is referred to as infodemiology [1,34] and gathers much attention in terms of practical needs.…”

Section: Introductionmentioning

confidence: 99%

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Wakamiya¹,

Kawai²,

Aramaki³

2018

JMIR Public Health Surveill

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BackgroundThe recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results.ObjectiveThis study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts.MethodsWe presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information.ResultsThe estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points).ConclusionsThe proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.

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“…House (Hong & Nadler, 2012 (Bruns & Highfield, 2013), the 2010 Swedish elections (Larsson & Moe, 2012), the 2011 Nigerian presidential election (Fink, Bos, Perrone, Liu, & Kopecky, 2013), the 2013 Italian general election (Vaccari et al, 2013), the German national elections (Kaczmirek et al, 2013;Tumasjan et al, 2010), the Belgium national elections (Verdegem, D'heer, & Mechant, 2013), the French presidential elections (Hanna et al, 2013), the 2011 presidential elections in Kyrgyzstan (Turdubaeva, 2013), the 2012 South Korean presidential election (Park, 2013), and the 2010 UK general election (specifically Scotland) (Baxter & Marcella, 2010;Baxter et al, 2011). …”

Section: Twitter Usage In Government and Electoral Contextsmentioning

confidence: 99%

One does not simply tweet without consequence: a study of the electoral contest in Ashgrove

Manfield¹

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Twitter is now a fixture of society, an online meeting place for people to air their grievances about human rights and civil liberty, discuss global warming, stalk celebrities and show off their cats. An unedited forum of comments that is a gold mine of free data for those who seek to monitor opinions on everything from mobile phone providers to voting preferences. It is now unusual for modern political candidates not to sustain a social media presence during their campaign in order to connect with their constituency. This study examines Twitter use by candidates in a single electoratethe seat of Ashgrove-during an election campaign in the Australian state of Queensland in March 2012. In particular, it looks at how Twitter user groups drive concept discussions around themes and concepts within the campaign, thereby exerting influence within the domain as part of the election process. Using a theory building approach, the data set made up of 35,000 tweets was analysed using text analytics software to reveal how Twitter can be used as a feedback mechanism for candidates, how user groups drive concept discussions on Twitter, the role of legacy media within this framework, and how the language of Twitter is a unique genre of communication.

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Twitter, Public Opinion, and the 2011 Nigerian Presidential Election

Cited by 25 publications

References 12 publications

Analyzing Political Communication with Digital Trace Data

Analyzing Political Communication with Digital Trace Data

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

One does not simply tweet without consequence: a study of the electoral contest in Ashgrove

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