From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

O’Connor, Brendan; Balasubramanyan, Ramnath; Routledge, Bryan R.; Smith, Noah A.

doi:10.1609/icwsm.v4i1.14031

Cited by 962 publications

(126 citation statements)

References 16 publications

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“…In fact, researchers have recently begun to use the content of Twitter messages to measure and predict real-world phenomena, including movie box office returns (Asur and Huberman 2010), elections (O'Connor et al 2010), and the stock market (Bollen, Mao, and Zeng 2010). While these studies show remarkable promise, one heretofore unanswered question is: Are Twitter users a representative sample of society?…”

Section: Introductionmentioning

confidence: 99%

Understanding the Demographics of Twitter Users

Mislove

Lehmann

Ahn

et al. 2021

ICWSM

251

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Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.

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Section: Introductionmentioning

confidence: 99%

Understanding the Demographics of Twitter Users

Mislove

Lehmann

Ahn

et al. 2021

ICWSM

251

View full text Add to dashboard Cite

show abstract

“…Pak and Patrick divided the individual tweets into positive, negative, or neutral categories that could better understand sentiments by the computer (Pak and Paroubek, 2010). O'Connor et al indicated that the sentiments in tweets reflect the public opinion on various topics in public opinion surveys (O'Connor et al, 2010). This study identified sentiment analysis as a more cost-effective option versus public opinion surveys.…”

Section: Introductionmentioning

confidence: 78%

Designing forecasting assistant of the Bitcoin price based on deep learning using the market sentiment analysis and multiple feature extraction

Fakharchian

2022

Preprint

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Nowadays, the issue of fluctuations in the price of digital Bitcoin currency has a striking impact on the profit or loss of people, international relations, and trade. Accordingly, designing a model that can take into account the various significant factors for predicting the Bitcoin price with the highest accuracy is essential. Hence, the current paper presents several Bitcoin price prediction models based on Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) using market sentiment and multiple feature extraction. In the proposed models, several parameters, including Twitter data, news headlines, news content, Google Trends, Bitcoin-based stock, and finance, are employed based on deep learning to make a more accurate prediction. Besides, the proposed model analyzes the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiments to examine the latest news of the market and cryptocurrencies. According to the various inputs and analyses of this study, several effective feature selection methods, including mutual information regression, Linear Regression, correlation-based, and a combination of the feature selection models, are exploited to predict the price of Bitcoin. Finally, a careful comparison is made between the proposed models in terms of some performance criteria like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), and coefficient of determination (R2). The obtained results indicate that the proposed hybrid model based on sentiments analysis and combined feature selection with MSE value of 0.001 and R2 value of 0.98 provides better estimations with more minor errors regarding Bitcoin price. This proposed model can also be employed as an individual assistant for more informed trading decisions associated with Bitcoin.

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“…Several machine learning methods, supervised and unsupervised, have been developed to predict such events. The linear regression models use simple features to predict the occurrence time of future events (O'Connor et al, 2010;Bollen et al, 2011;He et al, 2013;Arias et al, 2014). More advanced techniques use features such as topic-related keywords as input to support vector machines, LASSO, and multi-task learning approaches (Ritterman et al, 2009;Wang et al, 2012).…”

Section: Forecasting Civil Unrest and Geopolitical Eventsmentioning

confidence: 99%

Identifying geopolitical event precursors using attention-based LSTMs

Hossain

Harutyunyan

Ning

et al. 2022

Front. Artif. Intell.

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Forecasting societal events such as civil unrest, mass protests, and violent conflicts is a challenging problem with several important real-world applications in planning and policy making. While traditional forecasting approaches have typically relied on historical time series for generating such forecasts, recent research has focused on using open source surrogate data for more accurate and timely forecasts. Furthermore, leveraging such data can also help to identify precursors of those events that can be used to gain insights into the generated forecasts. The key challenge is to develop a unified framework for forecasting and precursor identification that can deal with missing historical data. Other challenges include sufficient flexibility in handling different types of events and providing interpretable representations of identified precursors. Although existing methods exhibit promising performance for predictive modeling in event detection, these models do not adequately address the above challenges. Here, we propose a unified framework based on an attention-based long short-term memory (LSTM) model to simultaneously forecast events with sequential text datasets as well as identify precursors at different granularity such as documents and document excerpts. The key idea is to leverage word context in sequential and time-stamped documents such as news articles and blogs for learning a rich set of precursors. We validate the proposed framework by conducting extensive experiments with two real-world datasets—military action and violent conflicts in the Middle East and mass protests in Latin America. Our results show that overall, the proposed approach generates more accurate forecasts compared to the existing state-of-the-art methods, while at the same time producing a rich set of precursors for the forecasted events.

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From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

Cited by 962 publications

References 16 publications

Understanding the Demographics of Twitter Users

Understanding the Demographics of Twitter Users

Designing forecasting assistant of the Bitcoin price based on deep learning using the market sentiment analysis and multiple feature extraction

Identifying geopolitical event precursors using attention-based LSTMs

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