An algorithm to find the best time to tweet

Abdullatif, Abdullatif M Al; Alsoghayer, Raid; AlMajhad, Esra M

doi:10.1109/iccvia.2015.7351786

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…RQ60. Deducing the best time to tweet [140] any information related to exoskeletons which might be helpful for the sales and marketing team of exoskeleton companies and/or manufacturers. RQ61.…”

Section: Rq4mentioning

confidence: 99%

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Thakur¹

2022

Preprint

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The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

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Section: Rq4mentioning

confidence: 99%

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Thakur¹

2022

Preprint

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“…RQ60. Deducing the best time to tweet [141] any information related to exoskeletons that might be helpful for the sales and marketing team of exoskeleton companies and/or manufacturers. RQ61.…”

Section: List Of 100 Research Questions Related To Exoskeletonsmentioning

confidence: 99%

Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Thakur

2022

SSRN Journal

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“…They found that most social networking is performed around midnight. Abdullatif et al [33] suggested an algorithm to find the best time to tweet, maintaining that because of the dynamic nature of social networking, tweets that are hidden to followers decrease engagement and activity. The study proposed a system to discover the best time to tweet based on active followers' behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

An Evaluation Study in Social Media Research: Key Aspects to Enhancing the Promotion of Efficient Organizations on Twitter

Brdesee

Alsaggaf

2021

Informatics

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As social media has shifted from traditional to modern technical patterns, organizations have sought to take advantage of the presence of beneficiaries on social networks. They may serve customers, display ads, and respond to queries on social media accounts such as Twitter. The implementation of these services required a scientific study considering: (1) how to attract beneficiaries, (2) attraction times, and (3) measurement of the impact of that attraction. This study aimed to address these three points through an analysis of data from an educational organization’s Twitter account. We found that the interaction rates with tweets increased in the evening, and we identified the best times for the organization to reach more followers. We examined five months of data (an entire semester), analyzing thousands of tweets and their associated impressions, types of responses, integration ratio, and account usage. We also discovered that the quality of tweets had an impact on attracting new followers, particularly when tweeting media such as photos, videos, and other types of content. Finally, this research serves as a resource for educational organizations on new ways to publish accounts and foster organizational growth through electronic media.

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An algorithm to find the best time to tweet

Cited by 4 publications

References 20 publications

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

An Evaluation Study in Social Media Research: Key Aspects to Enhancing the Promotion of Efficient Organizations on Twitter

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