Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

Cruickshank, Iain J.; Carley, Kathleen M.

doi:10.1007/s41109-020-00317-8

Cited by 41 publications

(38 citation statements)

References 49 publications

(79 reference statements)

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“…The Twitter data has been utilized in several recent research investigations [5][6][7][8][9]. The public availability of the tweets allows researchers to extract valuable conclusions from them [13].…”

Section: Use Of Social Media In Researchmentioning

confidence: 99%

“…We have collected 684,503 tweets within a two-week period from May 1st, 2021, through May 14th, 2021. Twitter data has been utilized in several recent research investigations [5][6][7]. Social media platforms have been found to support access to information, discuss and solve engineering problems, identify new trends and communicate science to a public audience [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

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The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

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Section: Use Of Social Media In Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

View full text Add to dashboard Cite

show abstract

“…The Twitter data has been utilized in several recent research investigations [5][6][7]. The public availability of the tweets allows researchers to extract valuable conclusions from them [8].…”

Section: Use Of Social Media In Researchmentioning

confidence: 99%

Social Media Analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Preprint

View full text Add to dashboard Cite

The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.

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“…Social media response to the ongoing pandemic has received significant research attention: (1) health misinformation (Memon and Carley, 2020;Hossain et al, 2020;Cinelli et al, 2020), ( 2) polarization (Cruickshank and Carley, 2020;KhudaBukhsh et al, 2021), (3) disease modeling (Li et al, 2020), etc. Counterhate measures along the line of counterspeech research (Benesch et al, 2016;Benesch, 2014;Mathew et al, 2018;Palakodety et al, 2020b) to combat Anti-Asian hate (Ziems et al, 2020), and community blame (Saha et al, 2021) has been studied.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 1st Workshop on NLP for Positive Impact

2021

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Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks -summarization and question generation -to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by finetuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.

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Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

Cited by 41 publications

References 49 publications

Social media analytics of the Internet of Things

Social media analytics of the Internet of Things

Social Media Analytics of the Internet of Things

Proceedings of the 1st Workshop on NLP for Positive Impact

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