Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study

Alothali, Eiman; Hayawi, Kadhim; Alashwal, Hany

doi:10.1007/978-3-030-59618-7_10

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Trendiness indicates to the extent to which a brand conveys the latest, up-to-date and popular information regarding the brand (Alothali et al, 2020;Naaman et al, 2011). Trendiness grabs the attention of users with the most recent report and fashions (Alothali et al, 2020;Naaman et al, 2011). Consumers perceived themselves as more up to date when using social media compared to traditional channels.…”

Section: Literature Reviewmentioning

confidence: 99%

Drivers of green brand experience and its impact on pro-environmental behavioural intentions

Asan

Antonio

2023

JMM

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Researchers have worked hard for many years to investigate the factors that influence pro-environmental behavior and still found inconsistent result of the predictors. Therefore, there is a need to identify other potential environmental behavior predictors. The intention of this study is to investigate the drivers of green brand experience and its impact on pro-environmental behavioural intentions. This study analyzes 229 customers of green beauty and personal care brand in Indonesia using quantitative method with non-probability purposive sampling technique. This study found that only interactivity and green informativeness in social media marketing management activities have positive and significant influence on the green brand experience. While entertainment, customization, and WOM have entertainment, customization, and WOM insignificant influence. This study also found that green brand experience has positive and significantly influenced customer co-creation participation, repurchase, as well as green word of mouth intention. The findings contribute to the development theory of brand experience and Technologies for the Pro-Ecological Action Model. This study also contributes to the management of social media activities in enhancing green brand experience and promoting pro-environmental behavioural intentions that benefit both the brand and the environment.

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Section: Literature Reviewmentioning

confidence: 99%

Drivers of green brand experience and its impact on pro-environmental behavioural intentions

Asan

Antonio

2023

JMM

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“…Thus, hashtags aid the interpretation of Twitter messages efficiently by lowering the risk of miscommunication, so analysis based on it will be relevant and highly accessible (Scott, 2015). These hashtags have been understood in the multidisciplinary field using quantitative techniques using data retrieval software (or data collection tool) (Alothali et al, 2020); however, the qualitative interpretation of hashtags is limited in tourism. The data required to understand the in-depth qualitative interpretation around tourism and Twitter can be done with a small sample as compared to quantitative studies (Shaheer, 2019).…”

Section: Datamentioning

confidence: 99%

Can destination image be ascertained from social media? An examination of Twitter hashtags

Nautiyal

Albrecht

Carr

2022

Tourism and Hospitality Research

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The role of Twitter hashtags in creating destination images is under-researched, and there is limited understanding of their usefulness in destination promotion. This paper examines the destination image of Rishikesh, India, based on an analysis of relevant tweets during the decade 2009–2019. The hashtags of resident, domestic and international Twitter users (about Rishikesh, India) were compared with the images of Rishikesh promoted by the relevant Regional Tourism Organisation (RTO: Uttarakhand Tourism) to identify and analyse any similarities or differences in the destination image promotion. The findings suggest that ‘yoga’ and ‘Ganges’ were the most prominent images associated with Rishikesh. However, the efforts of the RTO regarding promoting Rishikesh also focus on hashtags featuring adventure. This adventure image is also used by Indian Twitter users, whereas the residents and international Twitter users were less likely to use adventure-related hashtags that much. However, though residents, Indian and international Twitter users used almost similar natural landscape-related hashtags, RTO uses them unsubstantially. The results emphasise the importance of, and potential for, selected experiential and interpersonal hashtags when promoting destination images via Twitter, thus maximising the potential for destination marketing through this platform. At last, a hashtag strategy for destination promotion is also proposed based on the findings of this paper for focused exposure and increased searchability.

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“…In general, people use social media platforms for interacting with others via various type of posts, by following them and being followed. In some platforms like Twitter, trending topics are discussed on a daily basis [2]. Malicious individuals and organizations exploit the flexibility and power of social media to gain influence by creating fake automated accounts, often called social bots or sybil accounts and, in this study, social media bots.…”

Section: Introductionmentioning

confidence: 99%

Social media bot detection with deep learning methods: a systematic review

Hayawi

Saha²,

Masud

et al. 2023

Neural Comput & Applic

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Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

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Characteristics of Similar-Context Trending Hashtags in Twitter: A Case Study

Cited by 4 publications

References 34 publications

Drivers of green brand experience and its impact on pro-environmental behavioural intentions

Drivers of green brand experience and its impact on pro-environmental behavioural intentions

Can destination image be ascertained from social media? An examination of Twitter hashtags

Social media bot detection with deep learning methods: a systematic review

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