Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

Kagan, Vadim; Stevens, Andrew W.; Subrahmanian, V. S.

doi:10.1109/mis.2015.16

Cited by 73 publications

(33 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works have showed the potential of online social media, in particular of the microblogging platform Twitter, for analyzing the public sentiment in general [1][2][3][4] or to predict stock markets movements or sales performance [5][6][7][8][9]. With the increasing importance of Twitter in political discussions, a considerable number of studies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] also investigated the possibility to analyze political processes and predict political elections from data collected on Twitter. However, these initial investigations achieved only mixed results and engendered a number of critical studies [20,28,29] questioning their methods and findings.…”

mentioning

confidence: 99%

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Bovet

Morone

Makse

2018

Sci Rep

131

View full text Add to dashboard Cite

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite the large amount of work addressing this question, there has been no clear validation of online social media opinion trend with traditional surveys. Here we develop a method to infer the opinion of Twitter users by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to build an in-domain training set of the order of a million tweets. We validate our method in the context of 2016 US Presidential Election by comparing the Twitter opinion trend with the New York Times National Polling Average, representing an aggregate of hundreds of independent traditional polls. The Twitter opinion trend follows the aggregated NYT polls with remarkable accuracy. We investigate the dynamics of the social network formed by the interactions among millions of Twitter supporters and infer the support of each user to the presidential candidates. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of traditional surveys.

show abstract

mentioning

confidence: 99%

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Bovet

Morone

Makse

2018

Sci Rep

131

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…For example, [11] introduced the method of political elections forecasting in different countries based on Twitter messages. Another, more specific research forecasts the results of 2013 elections in Pakistan and 2014 elections in India [12].…”

Section: Related Workmentioning

confidence: 99%

Designation of Situation Model in Twitter using Maximal Frequent Sequences

Atyagina¹,

Ledeneva²,

García-Hernández³

2015

RCS

View full text Add to dashboard Cite

Hashtag is definitely one of the most significant features of Twitter which now is spread all over the social networking services. It can serve different functions, and one of the most important is the designation of situation models. Using the method of Maximal Frequent Sequences we proved that the main idea of all data of one hashtag can be described in two or three phrases as a summary processed using the given method. We demonstrate how the recognition of situation models can be done automatically and fast. Also this method can be used for analysis of hashtag combinations and reconstruction of concepts based on the results of 1-grams and 2-grams, as we presented in detailed example of analysis of the following hashtags: #GalaxyFamily, #RussianMeteor, #Grammys and #10Dec hashtags.

show abstract

Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

Cited by 73 publications

References 3 publications

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

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

Designation of Situation Model in Twitter using Maximal Frequent Sequences

Contact Info

Product

Resources

About