scholarly journals How scientists can take the lead in establishing ethical practices for social media research

2019 ◽  
Vol 26 (4) ◽  
pp. 311-313 ◽  
Author(s):  
Sherry Pagoto ◽  
Camille Nebeker
Keyword(s):  
Social Media ◽  
Scientific Community ◽  
Human Subjects ◽  
Research Misconduct ◽  
The United States ◽  
Ethical Practices ◽  
Social Media Data ◽  
Media Research ◽  
Social Media Research ◽  
Media Data

Abstract Social media use has become ubiquitous in the United States, providing unprecedented opportunities for research. However, the rapidly evolving research landscape has far outpaced federal regulations for the protection of human subjects. Recent highly publicized scandals have raised legitimate concerns in the media about how social media data are being used. These circumstances combined with the absence of ethical standards puts even the best intentioned scientists at risk of possible research misconduct. The scientific community may need to lead the charge in insuring the ethical use of social media data in scientific research. We propose 6 steps the scientific community can take to lead this charge. We underscore the important role of funding agencies and universities to create the necessary ethics infrastructure to allow social media research to flourish in a way that is pro-technology, pro-science, and most importantly, pro-humanity.

scholarly journals Navigating Big Data dilemmas: Feminist holistic reflexivity in social media research

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171880773 ◽  
Author(s):  
Cheryl Cooky ◽  
Jasmine R Linabary ◽  
Danielle J Corple
Keyword(s):  
Social Media ◽  
Big Data ◽  
Institutional Review Boards ◽  
Social Media Data ◽  
Institutional Review ◽  
Slow Adaptation ◽  
Private Research ◽  
Media Research ◽  
Social Media Research ◽  
Media Data

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain.

Accepting the challenges of social media research

2015 ◽  
Vol 39 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Katrin Weller
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Publication Output ◽  
Social Media Data ◽  
General Audience ◽  
Content Type ◽  
The Media ◽  
Media Research ◽  
Social Media Research ◽  
Media Data

Purpose – The purpose of this paper is to introduce a new viewpoint series, Monitoring the Media: Spotlight on Social Media Research, by providing an overview of the key challenges in social media research and some current initiatives in addressing them. Design/methodology/approach – The paper considers publication output from disciplines dealing with social media studies and summarises the key challenges as discussed in the broader research community. Findings – The paper suggests that challenges originate both from the interdisciplinary nature of social media research and from the ever-changing research landscape. It concludes that, whilst the community is addressing some challenges, others require more attention. Originality/value – The paper summarises key challenges of social media and will be of interest to researchers in different disciplines, as well as a general audience, wanting to learn about how social media data are used for research.

scholarly journals Sharing selves: Developing an ethical framework for curating social media data

2018 ◽  
Vol 12 (2) ◽  
pp. 196-209 ◽  
Author(s):  
Sara Mannheimer ◽  
Elizabeth A. Hull
Keyword(s):  
Social Media ◽  
Multiple Roles ◽  
Ethical Framework ◽  
Academic Librarians ◽  
Research Practice ◽  
Digital Repository ◽  
Social Media Data ◽  
Media Research ◽  
Social Media Research ◽  
Media Data

Open sharing of social media data raises new ethical questions that researchers, repositories and data curators must confront, with little existing guidance available. In this paper, the authors draw upon their experiences in their multiple roles as data curators, academic librarians, and researchers to propose the STEP framework for curating and sharing social media data. The framework is intended to be used by data curators facilitating open publication of social media data. Two case studies from the Dryad Digital Repository serve to demonstrate implementation of the STEP framework. The STEP framework can serve as one important ‘step’ along the path to achieving safe, ethical, and reproducible social media research practice.

scholarly journals Digital Trespass: Ethical and Terms-of-Use Violations by Researchers Accessing Data From an Online Patient Community (Preprint)

2018 ◽  
Author(s):  
Emil Chiauzzi ◽  
Paul Wicks
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Research Network ◽  
Media Ethics ◽  
Computer Data ◽  
Social Media Data ◽  
Media Research ◽  
Social Media Research ◽  
Media Data ◽  
Review Boards

UNSTRUCTURED With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

scholarly journals Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

2020 ◽  
Vol 17 (14) ◽  
pp. 4988 ◽  
Author(s):  
Diya Li ◽  
Harshita Chaudhary ◽  
Zhe Zhang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
San Francisco ◽  
Learning Algorithm ◽  
The United States ◽  
Spatiotemporal Pattern ◽  
Social Media Data ◽  
Clinical Patient ◽  
Stress Symptoms ◽  
Media Data

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

scholarly journals Next-generation visitation models using social media to estimate recreation on public lands

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Spencer A. Wood ◽  
Samantha G. Winder ◽  
Emilia H. Lia ◽  
Eric M. White ◽  
Christian S. L. Crowley ◽  
...  
Keyword(s):  
Social Media ◽  
Public Lands ◽  
The United States ◽  
Multiple Sources ◽  
Social Media Data ◽  
Visitor Management ◽  
Relative Value ◽  
Promising Source ◽  
Recreational Use ◽  
Media Data

Abstract Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

Social Media Relationships

2021 ◽  
Author(s):  
Umoloyouvwe Ejiro Onomake
Keyword(s):  
Social Media ◽  
Participant Observation ◽  
Multiple Relationships ◽  
Social Media Data ◽  
For Profit ◽  
Social Media Platforms ◽  
Media Research ◽  
Use Of Social Media ◽  
The Relationship ◽  
Media Data

Ethnography has been used to research various people and topics online, primarily using netnography and digital ethnography. Researchers and businesses employ digital ethnographic methods to access an assortment of social media platforms in order to learn about social media users. Researchers seek to understand relationships between social media users and organizations from both academic and practitioner perspectives. These organizations run the gamut from for-profit businesses, to nonprofits, nongovernmental organizations (NGOs), and government agencies. The specific focus here is on social media research as it relates to businesses. Organizations make use of social media in a variety of ways, but chiefly to market to clients and to gather information on followers; the latter of which, in turn, helps them understand their target markets. While this social media data is both quantitative and qualitative in nature, the emphasis here centers on qualitative data, particularly the ways businesses interact with social media users. While some firms mainly use older forms of one-way marketing that solely focus on disseminating information, other firms increasingly seek ways to interact with customers and co-create products with clients. Additionally, social media users are creating their own communities, formed due to a shared interest in a brand. Companies strive to learn more about their customers through these groups. Influencers also play a role in the relationship between organizations and social media users by linking their own followerships to products and brands. In turn, influencers develop their own relationships with organizations through sponsorships, thus becoming brands themselves. Influencers risk losing their followerships when followers perceive them as no longer accessible or authentic. This change in perception can occur for a variety of reasons, including when followers believe that an influencer has prioritized brand alignment over building connections with followers. Due to multiple relationships with different brands and their followers, influencers must negotiate the ambiguity and evolving nature of their role. As social media and digital spaces develop, so must the tools used by anthropologists. Anthropologists should remain open to incorporating hallmarks of ethnographic research such as fieldnotes, participant observation, and focus groups in new ways and alongside tools from other disciplines, including market and UX (user experience) research. The divide between practitioners and academics is blurring. Anthropologists can solve client issues while contributing their voices to larger anthropological and societal discussions.

scholarly journals Informed, Uninformed and Participative Consent in Social Media Research

2013 ◽  
Vol 55 (6) ◽  
pp. 791-808 ◽  
Author(s):  
Daniel Nunan ◽  
Baskin Yenicioglu
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Interpersonal Communication ◽  
Online Data ◽  
Consumer Privacy ◽  
The Social ◽  
Media Research ◽  
Social Media Research ◽  
Ethics Guidelines ◽  
Media Data

The use of online data is becoming increasingly essential for the generation of insight in today's research environment. This reflects the much wider range of data available online and the key role that social media now plays in interpersonal communication. However, the process of gaining permission to use social media data for research purposes creates a number of significant issues when considering compatibility with professional ethics guidelines. This paper critically explores the application of existing informed consent policies to social media research and compares with the form of consent gained by the social networks themselves, which we label ‘uninformed consent’. We argue that, as currently constructed, informed consent carries assumptions about the nature of privacy that are not consistent with the way that consumers behave in an online environment. On the other hand, uninformed consent relies on asymmetric relationships that are unlikely to succeed in an environment based on co-creation of value. The paper highlights the ethical ambiguity created by current approaches for gaining customer consent, and proposes a new conceptual framework based on participative consent that allows for greater alignment between consumer privacy and ethical concerns.

scholarly journals Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

2017 ◽  
Vol IV-4/W2 ◽  
pp. 139-146
Author(s):  
J. Ajayakumar ◽  
E. Shook ◽  
V. K. Turner
Keyword(s):  
Social Media ◽  
Extreme Events ◽  
The United States ◽  
Environmental Data ◽  
Data Sources ◽  
Social Media Data ◽  
Public Response ◽  
Spatio Temporal ◽  
Media Data ◽  
Media Responses

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

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