scholarly journals Topic Detection and Tracking Techniques on Twitter: A Systematic Review

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Meysam Asgari-Chenaghlu ◽  
Mohammad-Reza Feizi-Derakhshi ◽  
Leili Farzinvash ◽  
Mohammad-Ali Balafar ◽  
Cina Motamed

Social networks are real-time platforms formed by users involving conversations and interactions. This phenomenon of the new information era results in a very huge amount of data in different forms and modalities such as text, images, videos, and voice. The data with such characteristics are also known as big data with 5-V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. The importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also known as first-hand news reporting social media which has been a news reporting and informing platform even for political influencers or catastrophic news reporting. In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning-based methods.

2018 ◽  
Vol 87 ◽  
pp. 580-590 ◽  
Author(s):  
Meisong Wang ◽  
Prem Prakash Jayaraman ◽  
Ellis Solaiman ◽  
Lydia Y. Chen ◽  
Zheng Li ◽  
...  

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 3858-3870
Author(s):  
Chuanzhen Li ◽  
Minqiao Liu ◽  
Juanjuan Cai ◽  
Yang Yu ◽  
Hui Wang

2018 ◽  
Vol 10 (9) ◽  
pp. 3215 ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Valentina Ndou ◽  
Giustina Secundo

This paper aims to contribute to the debate on Open Innovation in the age of Big Data by shedding new light on the role that social networks can play as enabling platforms for tourists’ involvement and sources for the creation and management of valuable knowledge assets. The huge amount of data generated on social media by tourists related to their travel experiences can be a valid source of open innovation. To achieve this aim, this paper presents evidence of a digital tourism experience, through a longitudinal case study of a destination in Apulia, a Southern European region. The findings of the study demonstrate how social Big Data could open up innovation processes that could be of support in defining sustainable tourism experiences in a destination.


2022 ◽  
pp. 385-410
Author(s):  
Časlav Kalinić ◽  
Miroslav D. Vujičić

The rise of social media allowed greater people participation online. Platforms such as Facebook, Twitter, Instagram, or TikTok enable visitors to share their thoughts, opinions, photos, locations. All those interactions create a vast amount of data. Social media analytics, as a way of application of big data, can provide excellent insights and create new information for stakeholders involved in the management and development of cultural tourism destinations. This chapter advocates for the employment of the big data concept through social media analytics that can contribute to the management of visitors in cultural tourism destinations. In this chapter, the authors highlight the principles of big data and review the most influential social media platforms – Facebook, Twitter, Instagram, and TikTok. On that basis, they disclose opportunities for the management and marketing of cultural tourism destinations.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 98044-98056
Author(s):  
Wei Liu ◽  
Lei Jiang ◽  
Yusen Wu ◽  
Tingting Tang ◽  
Weimin Li

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