Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies

O’Leary, Daniel E.

doi:10.1002/isaf.1376

Cited by 52 publications

(24 citation statements)

References 60 publications

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“…Paul and Dredze (2011) also investigated how Twitter could be analysed to forecast diseases. O'Leary (2015) provides a recent summary of ‘Twitter mining’ research.…”

Section: Previous Literature: Google Searches Twitter Tweets and Wimentioning

confidence: 99%

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths

O’Leary

Storey

2020

Intell Sys Acc Fin Mgmt

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Forecasting the number of cases and the number of deaths in a pandemic provides critical information to governments and health officials, as seen in the management of the coronavirus outbreak. But things change. Thus, there is a constant search for real-time and leading indicator variables that can provide insights into disease propagation models. Researchers have found that information about social media and search engine use can provide insights into the diffusion of flu and other diseases. Consistent with this finding, we found that a model with the number of Google searches, Twitter tweets, and Wikipedia page views provides a leading indicator model of the number of people in the USA who will become infected and die from the coronavirus. Although we focus on the current coronavirus pandemic, other recent viruses have threatened pandemics (e.g. severe acute respiratory syndrome). Since future and existing diseases are likely to follow a similar search for information, our insights may prove fruitful in dealing with the coronavirus and other such diseases, particularly in the early phases of the disease.

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“…Paul and Dredze (2011) also investigated how Twitter could be analysed to forecast diseases. O'Leary (2015) provides a recent summary of ‘Twitter mining’ research.…”

Section: Previous Literature: Google Searches Twitter Tweets and Wimentioning

confidence: 99%

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths

O’Leary

Storey

2020

Intell Sys Acc Fin Mgmt

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“…O'Leary () underlined the importance of ‘Twitter mining’ by synthesizing the extant literature in using Twitter information for prediction, discovery and causation investigation.…”

Section: Related Workmentioning

confidence: 99%

Big data tools for Islamic financial analysis

Mnif

Jarboui

Hassan

et al. 2020

Intell Sys Acc Fin Mgmt

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Summary Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.

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“…However, it is not clear from this research the extent to which that activity holds. Fourth, the text dictionaries in this paper could be further extended based on consideration of larger text corpuses (Fisher, Garnsey, & Hughes, , Goel & Uzuner, , O'Leary, ). Such extensions could make the dictionaries broader and more encompassing to facilitate broader use.…”

Section: Summary Contribution and Extensionsmentioning

confidence: 99%

Gathering and evaluating innovation ideas using crowdsourcing: Impact of the idea title and the description on the number of votes in each phase of a two‐phase crowdsourcing project

Moffitt

O’Leary

2019

Expert Systems

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Organizations are using crowdsourcing to capture innovation knowledge from the crowd in the form of ideas and then using the crowd to evaluate those ideas using votes. In this paper, we investigate a crowdsourcing setting in which Canada solicited information from its citizens to develop a digital transformation strategy. Canada used a two‐phase approach. Phase 1 was used to determine which ideas had the largest number of crowd votes, whereas in Phase 2, the crowd voted on the 30 leading vote‐getting ideas to determine the three winning ideas. This research investigates the ability to use information from ideas to estimate the number of votes that the ideas generate. This approach could be used to estimate the number of ideas, before making information available to the crowd. The unstructured text information in the idea is structured by using target concept dictionaries, which are used to estimate the extent to which the dictionary words appear in the ideas (e.g., “globalism”) and are related to the number of votes. Using this approach, roughly 1% of the total words are used to explain roughly 60% of the variance in the votes. Further, we also find that the variables associated with Phase 1 votes are not the same variables associated with Phase 2 votes; that is, the decision‐making variables changed. Finally, we find that votes are statistically significantly related to the content in the idea titles and the idea statements.

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Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies

Cited by 52 publications

References 60 publications

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths

Big data tools for Islamic financial analysis

Gathering and evaluating innovation ideas using crowdsourcing: Impact of the idea title and the description on the number of votes in each phase of a two‐phase crowdsourcing project

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