scholarly journals Measuring Americans’ Comfort With Research Uses of Their Social Media Data

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110338
Author(s):  
Sarah Gilbert ◽  
Jessica Vitak ◽  
Katie Shilton
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Data Use ◽  
Data Types ◽  
Sensitive Data ◽  
Social Media Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Diverse Data ◽  
Media Data

Research using online datasets from social media platforms continues to grow in prominence, but recent research suggests that platform users are sometimes uncomfortable with the ways their posts and content are used in research studies. While previous research has suggested that a variety of contextual variables may influence this discomfort, such factors have yet to be isolated and compared. In this article, we present results from a factorial vignette survey of American Facebook users. Findings reveal that researcher domain, content type, purpose of data use, and awareness of data collection all impact respondents’ comfort—measured via judgments of acceptability and concern—with diverse data uses. We provide guidance to researchers and ethics review boards about the ways that user reactions to research uses of their data can serve as a cue for identifying sensitive data types and uses.

scholarly journals Review of Data Visualization for Social Media Postings

2018 ◽  
Vol 7 (4.38) ◽  
pp. 939
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Hamizah Binti Anuar
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Line Graph ◽  
Data Types ◽  
Social Media Data ◽  
Data Analyst ◽  
The Social ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Media Data

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.      

Harnessing social media to understand tourist mobility: the role of information technology and big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Current Trend ◽  
Social Media Data ◽  
Content Type ◽  
Use Of Data ◽  
Social Media Platforms ◽  
The City ◽  
Media Data

Purpose This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions. Design/methodology/approach This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions. Findings This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them. Originality/value The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

scholarly journals How social media reshapes action on distant customers: some empirical evidence

2017 ◽  
Vol 30 (4) ◽  
pp. 777-794 ◽  
Author(s):  
Deborah Agostino ◽  
Yulia Sidorova
Keyword(s):  
Social Media ◽  
Joint Control ◽  
Control Activity ◽  
Social Media Data ◽  
Content Type ◽  
Corporate Website ◽  
Social Media Platforms ◽  
The One ◽  
Centres Of Calculation ◽  
Media Data

Purpose The purpose of this paper is to investigate how centres of calculation, now emerging in connection with social media, impact on the process of acting on distant customers. Specifically, the authors are interested in exploring how the distance between the organization and its customer is affected and how knowledge is accumulated within this centre. Design/methodology/approach A case study in an Italian telecommunication company was conducted over a time horizon of two years, analysing data sources in the form of interviews, documents and reports, corporate website, social media platforms and participants’ observations. With the adoption of social media, the company configured a new centre of calculation, called monitoring room, in the attempt to accumulate knowledge about its customers. The authors unpacked the activity of the centre of calculation discussing its ability to perform action upon a distant periphery and the process of knowledge accumulation inside the centre itself. Findings The results highlight the implication of social media for “action at a distance”. On the one hand, social media blurs the distinction between the centre and a periphery giving rise to a de-centring, and stimulating a joint control activity between the customer and the organization. On the other hand, social media was found vulnerable in providing a unique knowledge about customers: accumulation cycles that exploit social media data can be replicated by users with skills in data analytics and the knowledge they provide might conflict with knowledge provided by traditional data. Originality/value The authors contribute to an emergent stream of literature that is investigating accounting implications derived from social media, by underlying the controversial effects connected with centres of calculation enacted by social media data. The authors suggest that, while social media data provide the organization with huge amount of information real time, at the same time, it contributes to de-centring allowing customers and external actors to act upon the organization, rather than improving knowledge inside the centre.

scholarly journals Habitual Disclosure: Routine, Affordance, and the Ethics of Young Peoples Social Media Data Surveillance

2020 ◽  
Vol 6 (2) ◽  
pp. 205630512091561
Author(s):  
Clare Southerton ◽  
Emmeline Taylor
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Data Privacy ◽  
Qualitative Interviews ◽  
Photo Elicitation ◽  
Active Engagement ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Data Surveillance ◽  
Media Data

Drawing on findings from qualitative interviews and photo elicitation, this article explores young people’s experiences of breaches of trust with social media platforms and how comfort is re-established despite continual violations. It provides rich qualitative accounts of users habitual relations with social media platforms. In particular, we seek to trace the process by which online affordances create conditions in which “sharing” is regarded as not only routine and benign but pleasurable. Rather it is the withholding of data that is abnormalized. This process has significant implications for the ethics of data collection by problematizing a focus on “consent” to data collection by social media platforms. Active engagement with social media, we argue, is premised on a tentative, temporary, shaky trust that is repeatedly ruptured and repaired. We seek to understand the process by which violations of privacy and trust in social media platforms are remediated by their users and rendered ordinary again through everyday habits. We argue that the processes by which users become comfortable with social media platforms, through these routines, call for an urgent reimagining of data privacy beyond the limited terms of consent.

A scholarly divide: Social media, Big Data, and unattainable scholarship

First Monday ◽  
2016 ◽  
Author(s):  
Asta Zelenkauskaite ◽  
Erik P. Bucy
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Accuracy ◽  
Data Types ◽  
Social Data ◽  
Social Media Data ◽  
Storage And Retrieval ◽  
Social Media Platforms ◽  
And Control ◽  
Media Data

Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the ‘Big Data’ paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze — and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing ‘scholarly divide’ between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.

scholarly journals Informed consent for linking survey and social media data - Differences between platforms and data types

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Johannes Breuer ◽  
Tarek Al Baghal ◽  
Luke Sloan ◽  
Libby Bishop ◽  
Dimitra Kondyli ◽  
...  
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Data Collection ◽  
Case Studies ◽  
Survey Data ◽  
Data Types ◽  
Social Media Data ◽  
Key Aspects ◽  
Media Data ◽  
Collection Methods

Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data.

A framework for understanding citizens’ political participation in social media

2019 ◽  
Vol 33 (4) ◽  
pp. 1053-1075
Author(s):  
Vidushi Pandey ◽  
Sumeet Gupta ◽  
Manojit Chattopadhyay
Keyword(s):  
Social Media ◽  
Political Participation ◽  
Text Classification ◽  
Consumer Participation ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Media Data

Purpose The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society. Design/methodology/approach This study is based on Teorell et al.’s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms. Findings The analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline. Research limitations/implications The current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required. Originality/value To the best of the authors’ knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012; Shirazi, 2013; Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.

Taking archives to the people

2019 ◽  
Vol 37 (3) ◽  
pp. 374-388 ◽  
Author(s):  
Jonathan Mukwevho ◽  
Mpho Ngoepe
Keyword(s):  
South Africa ◽  
Social Media ◽  
Data Collection ◽  
Survey Method ◽  
Web Based ◽  
Content Type ◽  
The People ◽  
Social Media Platforms ◽  
Public Archives ◽  
Diverse Data

Purpose Despite the availability of the mandate of public archives repositories to “take archives to the people in South Africa”, archives in South Africa remain largely the domain of the elite. The purpose of this paper is to investigate the adoption of social media in South Africa as a tool for taking archives to the people especially young people between the age of 13 and 34. The researchers confined the study to all ten public archives repositories in South Africa. Design/methodology/approach This quantitative study collected data using survey questionnaires and web-based content analysis of social media presence of public archives repositories. Findings The study revealed that few public archives repositories are using Facebook, followed by Twitter and LinkedIn to engage users. The public archives repositories rely mostly on social media platforms operated by their mother bodies as they are subsidiary units within arts and culture departments in government. As a result, public archives repositories are restricted to operate their own accounts on social media. It is argued that public archives should be allowed by their mother departments to operate their own accounts on social media platforms. Failure to change this restriction could lead to public archival institutions continuing to take archives away from the people, instead of taking archives to the people. Research limitations/implications The study sought to provide useful practical implications for public archives repositories as it would serve as a benchmarking tool to enable the development and reporting on the visibility and accessibility of archival material, and thus ensure an increased public knowledge of archives. Originality/value The study triangulated data collection instruments that helped to collect as much and as diverse data as possible, which generated the best possible insights into the phenomenon of interest. Previous similar studies in South Africa utilised only survey method with either interviews or questionnaires as data collection tools.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

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