Using Social Media to Predict the Future: A Systematic Literature Review

Phillips, Lawrence S.; Dowling, Chase; Shaffer, Kyle; Hodas, Nathan O.; Volkova, Svitlana

doi:10.48550/arxiv.1706.06134

Cited by 10 publications

(10 citation statements)

References 142 publications

(371 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Potentials of Digital Traces. Web and social media data has been leveraged to 'predict the future' in many areas such as politics, health, and economics (Askitas and Zimmermann, 2015;Phillips et al, 2017). It is also considered as a means for learning about the present, e.g., for gaining insights into human behavior and opinions, such as using search queries to examine agenda-setting effects (Ripberger, 2011), leveraging Instagram to detect drug use (Yang and Luo, 2017), and measure consumer confidence through Twitter (Pasek et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

From the total survey error framework to an error framework for digital traces of humans

Sen

Flöck

Weller

et al. 2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

The interactions and activities of hundreds of millions of people worldwide are recorded as digital traces every single day. When pulled together, these data offer increasingly comprehensive pictures of both individuals and groups interacting on different platforms, but they also allow inferences about broader target populations beyond those platforms, representing an enormous potential for the Social Sciences. Notwithstanding the many advantages of digital traces, recent studies have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena. Incidentally, many similar errors also affect survey estimates, which survey designers have been addressing for decades using error conceptualization frameworks such as the Total Survey Error Framework. In this work, we propose a conceptual framework to diagnose, understand and avoid errors that may occur in studies that are based on digital traces of humans leveraging the systematic approach of the Total Survey Error Framework.

show abstract

Section: Related Workmentioning

confidence: 99%

From the total survey error framework to an error framework for digital traces of humans

Sen

Flöck

Weller

et al. 2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

show abstract

“…There are several other surveys on societal event predictions. Phillips et al [97] provided a literature review that examines the problems and techniques for predictive analysis using social media data. Recently, Zhao [157] provided a systematic survey of existing data-driven event prediction methods, covering challenges, techniques, applications, evaluations, and open problems.…”

Section: Previous Work and Contributionsmentioning

confidence: 99%

A Survey on Societal Event Forecasting with Deep Learning

Deng¹,

Ning²

2021

Preprint

View full text Add to dashboard Cite

Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: civil unrest and crime. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.

show abstract

“…Fülöp et al [81] provided a survey and categorization of applications that utilize predictive analytics techniques to perform event processing and detection, while Jiang [105] focused on spatial prediction methods that predict the indices that have spatial dependency. Baklr et al [17] summarized the literature on predicting structural data such as geometric objects and networks, and Arias et al [12] Phillips et al [163], and Yu and Kak [232] all proposed the techniques for predictive analysis using social data.…”

Section: Related Surveysmentioning

confidence: 99%

Event Prediction in the Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

View full text Add to dashboard Cite

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

show abstract

Using Social Media to Predict the Future: A Systematic Literature Review

Cited by 10 publications

References 142 publications

From the total survey error framework to an error framework for digital traces of humans

From the total survey error framework to an error framework for digital traces of humans

A Survey on Societal Event Forecasting with Deep Learning

Event Prediction in the Big Data Era: A Systematic Survey

Contact Info

Product

Resources

About