Social media prediction: a literature review

Rousidis, Dimitrios; Koukaras, Paraskevas; Tjortjis, Christos

doi:10.1007/s11042-019-08291-9

Cited by 66 publications

(34 citation statements)

References 72 publications

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“…Digital technologies are used in digital health initiatives including various types of technology, including those designed for information sharing, communication, clinical decision support, ‘digital therapies’, patient and/or population monitoring and control, bioinformatics and personalized medicine, and service user health informatics (Coiera, 2015 ). A survey showed that the vast number of experts, more than 75 %, use Twitter data, and more than half prefer to use regression algorithms to do social media prediction, but not all forecasting models can predict accurately, and prediction appears to be reliable on the affiliated field (Rousidis et al, 2020 ). Although the social media population comprises only a specific fraction of the population, the reach of its posts can cover broader impacts through social multiplier effects (McClellan et al, 2017 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several existing pieces of literature have researched health-related studies associated with AI use on social media through the following ways: simple frequency analysis, content analysis, semantic analysis, supervised learning, and a major analytic approach (i.e., time series analysis and forecasting techniques) (Briand et al, 2018;McClellan et al, 2017;D'Alfonso, 2020;Rousidis et al, 2020). The time series analysis has some limitations such as the lack of ability to recognize sarcastic or humorous tweets, but it could be refined if combines with sentiment analysis (McClellan et al, 2017).…”

Section: Autonomymentioning

confidence: 99%

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The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

2021

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Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Autonomymentioning

confidence: 99%

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

2021

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“…El utilizar los argumentos basados en el comportamiento de los likes/reacciones de los usuarios, representa la fuerza del vínculo social en un contexto de comportamiento de rebaño y proporciona una visión sobre el comportamiento de los usuarios de las redes sociales y la influencia social (Meng et al, 2020). Así mismo, la Minería de opiniones se ocupa de analizar las opiniones, criticas, actitudes y emociones de las personas hacia diferentes marcas, empresas, productos e incluso individuos usando algoritmos de preprocesamiento de texto (Rousidis et al, 2020).…”

Section: Enfoques Y Modelos De Análisis Para Social Mediaunclassified

Identificación de áreas de aplicación de arquitecturas de software basadas en modelos, técnicas y herramientas de social media

Velazquez-Solis¹,

Flores-Ríos²,

Ibarra-Esquer³

et al. 2021

risti

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Social media se refiere al conjunto de técnicas y herramientas que están tomando relevancia para la recolección de información sobre el comportamiento de los usuarios en las redes sociales. El objetivo de esta investigación fue identificar los enfoques, modelos de análisis, técnicas y herramientas de social media aplicados en arquitecturas de software, con el fin de detectar las áreas de aplicación por medio de estudios empíricos. Para ello, se realizó un mapeo sistemático de la literatura con el cual se seleccionaron treinta artículos publicados entre 2015 y 2020. Los resultados validaron que en los últimos tres años se han incrementado en un 57% las publicaciones, principalmente en el área de investigación centrándose en diseñar arquitecturas de software que incorporen modelos de análisis de social media en los componentes y crear nuevas oportunidades en las redes sociales.

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“…However, as it happens in related fields [38], the preferences of the users over time could be biased by several factors, not reflecting their real preferences [39]. A systematic overview has been provided in [40], where the authors reviewed the recent literature, offering statistics and discussing about methods, algorithms, techniques, and challenges.…”

Section: Popularity In Social Mediamentioning

confidence: 99%

Popularity Prediction of Instagram Posts

Carta¹,

Podda²,

Recupero³

et al. 2020

Preprint

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Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency and is general enough to be applied to other social media as well.

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Social media prediction: a literature review

Cited by 66 publications

References 72 publications

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

Identificación de áreas de aplicación de arquitecturas de software basadas en modelos, técnicas y herramientas de social media

Popularity Prediction of Instagram Posts

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