Expanding the Boundaries of Your Research Using Social Media: Stand-Up and Be Counted

Kumar, M. Jagadesh

doi:10.1080/02564602.2014.944442

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Wider adoption of SoMe by researchers to communicate health information has the potential to impact on public opinion and to address misinformation, particularly in relation to public health issues (Foulkes, 2011). Blogging research can also help its discoverability, making it more likely to come up in Internet search engines; it may also lead to more accurate reporting in the media (Kumar, 2014). Furthermore, sharing of conference research presentations on Slideshare, or publishing podcasts and videos to YouTube can also help to better inform the public on health issues.…”

Section: Disseminationmentioning

confidence: 98%

Perspectives on social media in and as research: A synthetic review

Lafferty

Manca

2015

International Review of Psychiatry

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With the growth of social media use in both the private and public spheres, researchers are currently exploring the new opportunities and practices offered by these tools in the research lifecycle. This area is still in its infancy: As methodological approaches and methods are being tested - mainly through pragmatic and exploratory approaches - practices are being shaped and negotiated by the actors involved in research. A further element of complexity is added by the ambivalent status of social media within research activities. They can be both a tool - for recruitment, data collection, analysis - and data - as what constitutes the corpus to be analysed - both in an observational and interactive domain. This synthetic analysis of the literature is aimed at identifying how social media are currently being used in research and how they fit into the research lifecycle. We identify and discuss emerging evidence and trends in the adoption of social media in research, which can be used and applied by psychiatry research practitioners as a framework to inform the development of a personalized research network and social media strategy in research.

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Section: Disseminationmentioning

confidence: 98%

Perspectives on social media in and as research: A synthetic review

Lafferty

Manca

2015

International Review of Psychiatry

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“…The content on social media is filled with the experiences and opinions of products and life [14]. When consumers want to buy new products or services, they often seek opinions from close friends and family members [15]. In the context of microblogs, consumers often reveal their perceptions about products in the form of feelings and reactions [16].…”

Section: Identifying Emotional Expressionsmentioning

confidence: 99%

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Jussila¹,

Alkhammash²,

Alghamdi³

et al. 2022

Computers, Materials &Amp; Continua

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Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users' stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights. We analyzed a case study of a marketing strategy on the Twitter platform. Participants in the marketing campaign shared photos and texts about their stress management practices. Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants. The computational semantic analysis of the tweets was compared to the text analysis of the tweets. The content analysis of only tweet images resulted in 96% accuracy in detecting tweet context, while that of the textual content of tweets yielded an accuracy of 91%. Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

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“…Social interactions in these networks are crammed full with positive and negative relations. Positive relations are formed by support, endorsement and friendship and thus, create a network of well-connected users which is useful for the promotion of market products, brands, services and new research ideas on social media [2]. Negative relations, on the other hand, are a result of opposition, distrust, antagonism and avoidance.…”

Section: Introductionmentioning

confidence: 99%

Analyzing negative ties in social networks: A survey

Kaur

Singh

2016

Egyptian Informatics Journal

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Online social networks are a source of sharing information and maintaining personal contacts with other people through social interactions and thus forming virtual communities online. Social networks are crowded with positive and negative relations. Positive relations are formed by support, endorsement and friendship and thus, create a network of well-connected users whereas negative relations are a result of opposition, distrust and avoidance creating disconnected networks. Due to increase in illegal activities such as masquerading, conspiring and creating fake profiles on online social networks, exploring and analyzing these negative activities becomes the need of hour. Usually negative ties are treated in same way as positive ties in many theories such as balance theory and blockmodeling analysis. But the standard concepts of social network analysis do not yield same results in respect of each tie. This paper presents a survey on analyzing negative ties in social networks through various types of network analysis techniques that are used for examining ties such as status, centrality and power measures. Due to the difference in characteristics of flow in positive and negative tie networks some of these measures are not applicable on negative ties. This paper also discusses new methods that have been developed specifically for analyzing negative ties such as negative degree, and h * measure along with the measures based on mixture of positive and negative ties. The different types of social network analysis approaches have been reviewed and compared to determine the best approach that can appropriately identify the negative ties in online networks. It has been analyzed that only few measures such as Degree and PN centrality are applicable for identifying outsiders in network. For applicability in online networks, the performance of PN measure needs to be verified and further, new measures should be developed based upon negative clique concept.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Expanding the Boundaries of Your Research Using Social Media: Stand-Up and Be Counted

Cited by 8 publications

References 3 publications

Perspectives on social media in and as research: A synthetic review

Perspectives on social media in and as research: A synthetic review

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Analyzing negative ties in social networks: A survey

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