“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective

Grover, P. S.; Kar, Arpan Kumar; Davies, Gareth

doi:10.1016/j.ijinfomgt.2018.07.003

Cited by 114 publications

(64 citation statements)

References 100 publications

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“…Prior work strongly shows that SMA can provide important insights around disciplines such as marketing [14], in deliberation on political [15] and social concerns [16], in emergency [17], for building public relation [18] or even ruling out possibility of polarization of any major event [19]. Social media platforms like Twitter can also help us to gain insights into any specific domain [20] or technology [21]. However, in the domain of social media, penetration and diffusion of communication has three strong pillars, firstly managing and controlling misinformation [22], secondly use machine learning to automatically predicting the information (tweet) diffusion speed, scale, range and trend [23] and thirdly use frameworks to evaluate the credibility of a tweet, i.e.…”

Section: Information Diffusion and Social Media Analyticsmentioning

confidence: 99%

Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features

Kushwaha

Kar

Ilavarasan

2020

Lecture Notes in Computer Science

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Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.

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Section: Information Diffusion and Social Media Analyticsmentioning

confidence: 99%

Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features

Kushwaha

Kar

Ilavarasan

2020

Lecture Notes in Computer Science

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“…Crowdsourcing [6], [55], [79]- [81] and digital volunteers [82]- [84] are the additional areas identified from the literature. Furthermore, crisis communication [33]- [35], [38], [85]- [88] is another area of research that is further divided into social network [89], [90], issue arena which involve places for societal discussion on social media [90], [91], others areas include crisis responses in the form of information dissemination [87], [92]- [94] and information seeking [67], [95]- [100]. The effectiveness of social media for information dissemination and sharing has been discussed widely by [101], and [102] studies focus on the information dissemination dynamics, and their influence on emergency management.…”

Section: Q2 What Is the Taxonomy Of Research Studies Conducted Inmentioning

confidence: 99%

Crisis Informatics in the Context of Social Media Crisis Communication: Theoretical Models, Taxonomy, and Open Issues

et al. 2020

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The involvement application and use of crisis and emergency management and communication are increasing rapidly. This study conducts a systematic literature review to identify the development of theoretical models in the area of social media crisis communication and management. The study aims to review and analyse the relationship of social media-based crisis communication in the context of crisis informatics and its taxonomy and the related crisis communication theoretical models to derive the challenges and limitations. A total of 207 articles were selected for the evaluation based on quality, relevancy, and contribution. The findings revealed that the situational crisis communication theory (SCCT) is the most dominant theory, followed by social-mediated crisis communication (SMCC) and integrated crisis mapping (ICM) models. The study identified theories such as the STREMII model, social media crisis management matrix/framework (SMSMF), and an interactive crisis communication model (ICCM) as emerging models. Moreover, the result of the finding shows that stakeholder interaction is an understudied field, while information reliability and processing for decision-making purposes, the wider application of social media sites, privacy issues, and how social media interaction can improve community resilience or build stakeholders relationships remain suitable topics for future research.

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“…Technologies are not neutral or passive objects but rather shape the environment and provoke social dynamics as a result of their existence and necessity for human survival. Previous studies reveal that technologies have the potential to influence social, cultural and economic contexts and improve healthcare quality for communities when employed in the healthcare domain [24]. Expectedly, the use of mobile technology in South Africa in the healthcare system has also increased, particularly among CHWs [25].…”

Section: Technical Aspectmentioning

confidence: 99%

Categorization of Factors Influencing Community Health Workers from a Socio-Technical Systems Perspective

Ratshidi¹,

Grobbelaar²,

Botha³

2020

Lecture Notes in Computer Science

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In low-and-middle-income countries (LMICs), community health workers (CHWs) are often seen as a connecting bridge between two dynamic and overlapping systems-the community and formal health systems. Although the importance of CHWs is acknowledged, there is minimal aggregated evidence contributing towards understanding their position, technological capabilities, barriers and facilitators of their effectiveness in the South African context. Despite the widespread enthusiasm around the potential that mobile health (mHealth) technology holds in extending healthcare through CHW to underserved communities, an understanding of mHealth's various implications in a developing world context is imperative to appreciate both the community and health systems context. The CHWs within this context need to assume multiple roles as they work and live amongst and in the community. The study argues that by examining their multiple roles as part of the healthcare continuum and from within the community setting, appropriating technological solutions can be conceptualized to facilitate and enhance their impact and visibility. This research article then aims to articulate the key conceptual factors which should be considered when implementing technological solutions for CHWs within the South African context. The aim is operationalized by means of the best-fit framework synthesis method to explore the body of knowledge towards presenting a conceptual understanding through a categorization of Factors Influencing Community Health Workers from a Socio-Technical Systems Perspective.

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“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective

Cited by 114 publications

References 100 publications

Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features

Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features

Crisis Informatics in the Context of Social Media Crisis Communication: Theoretical Models, Taxonomy, and Open Issues

Categorization of Factors Influencing Community Health Workers from a Socio-Technical Systems Perspective

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