Social sensor analytics: Making sense of network models in social media

Dowling, Chase; Harrison, Joshua J.; Sathanur, Arun V.; Sego, Landon H.; Corley, Courtney D.

doi:10.1109/isi.2015.7165956

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Further, SM data is biased [155,167]. Although we can think of SM users as sensors of the real world [47,62,168], it is well known that SM users are not representative of the total population [155] and although many SM posts describe what is going on in a person's daily life [95], these posts are not necessarily representative of everything going on in the world around the user. As noted by [100,176,204,216], research in SM is marred by issues of generalizability, research which makes accurate predictions on one data set may not prove useful in another.…”

Section: Discussionmentioning

confidence: 99%

“…A significant manifestation of an event on SM, however, does not appear to be a sufficient condition for successful prediction. Take for instance the 2014 World Cup; the tournament saw global SM presence representing participating teams from around the world [48,62,81,161,217]. An attempt to predict match outcomes utilizing Twitter data, [161] failed to perform better than random chance for early tournament matches, and under-performed popular sports analysis agencies' predictions beyond quarter-final matches.…”

Section: Underlying Complexity Of Sm-based Modelsmentioning

confidence: 99%

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

Phillips¹,

Dowling²,

Shaffer³

et al. 2017

Preprint

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Social media (SM) data provides a vast record of humanity's everyday thoughts, feelings, and actions at a resolution previously unimaginable. Because user behavior on SM is a reflection of events in the real world, researchers have realized they can use SM in order to forecast, making predictions about the future. The advantage of SM data is its relative ease of acquisition, large quantity, and ability to capture socially relevant information, which may be difficult to gather from other data sources. Promising results exist across a wide variety of domains, but one will find little consensus regarding best practices in either methodology or evaluation. In this systematic review, we examine relevant literature over the past decade, tabulate mixed results across a number of scientific disciplines, and identify common pitfalls and best practices. We find that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks. But despite these shortcomings, recurring findings and promising results continue to galvanize researchers and demand continued investigation. Based on the existing literature, we identify research practices which lead to success, citing specific examples in each case and making recommendations for best practices. These recommendations will help researchers take advantage of the exciting possibilities offered by SM platforms.

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Section: Discussionmentioning

confidence: 99%

Section: Underlying Complexity Of Sm-based Modelsmentioning

confidence: 99%

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

Phillips¹,

Dowling²,

Shaffer³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

Local spatial obesity analysis and estimation using online social network sensors

Sun

Wang

et al. 2018

Journal of Biomedical Informatics

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Recently, the online social networks (OSNs) have received considerable attentions as a revolutionary platform to offer users massive social interaction among users that enables users to be more involved in their own healthcare. The OSNs have also promoted increasing interests in the generation of analytical, data models in health informatics. This paper aims at developing an obesity identification, analysis, and estimation model, in which each individual user is regarded as an online social network 'sensor' that can provide valuable health information. The OSN-based obesity analytic model requires each sensor node in an OSN to provide associated features, including dietary habit, physical activity, integral/incidental emotions, and self-consciousness. Based on the detailed measurements on the correlation of obesity and proposed features, the OSN obesity analytic model is able to estimate the obesity rate in certain urban areas and the experimental results demonstrate a high success estimation rate. The measurements and estimation experimental findings created by the proposed obesity analytic model show that the online social networks could be used in analyzing the local spatial obesity problems effectively.

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Social sensor analytics: Making sense of network models in social media

Cited by 2 publications

References 21 publications

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

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

Local spatial obesity analysis and estimation using online social network sensors

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