Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse

Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse. This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators. This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena

Instagram, Flickr, or Twitter : Assessing the usability of social media data for visitor monitoring in protected areas

Social media data is increasingly used as a proxy for human activity in diferent environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media based monthly visitation patterns match relatively well with the ofcial visitor counts. However, there were considerable diferences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identifed potential reasons why social media data and visitor statistics might not match: the geography and profle of the park, the visitor profle, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.Peer reviewe

Data Linking - Linking survey data with geospatial, social media, and sensor data (Version 1.0)

Survey data are still the most commonly used type of data in the quantitative social sciences. However, as not everything that is of interest to social scientists can be measured via surveys, and the self-report data they provide have certain limitations, such as recollection or social desirability bias, researchers have increasingly used other types of data that are not specifically created for research. These data are often called "found data" or "non-designed data" and encompass a variety of different data types. Naturally, these data have their own sets of limitations. One way of combining the unique strengths of survey data and these other data types and dealing with some of their respective limitations is to link them. This guideline first describes why linking survey data with other types of data can be useful for researchers. After that, it focuses on the linking of survey data with three types of data that are becoming increasingly popular in the social sciences: geospatial data, social media data, and sensor data. Following a discussion of the advantages and challenges associated with linking survey data with these types of data, the guideline concludes by comparing their similarities, presenting some general recommendations regarding linking surveys with other types of (found/non-designed) data, and providing an outlook on current developments in survey research with regard to data linking

Digital methods in a post-API environment

Qualitative and mixed methods digital social research often relies on gathering and storing social media data through the use of APIs (Application Programming Interfaces). In past years this has been relatively simple, with academic developers and researchers using APIs to access data and produce visualisations and analysis of social networks and issues. In recent years, API access has become increasingly restricted and regulated by corporations at the helm of social media networks. Facebook (the corporation) has restricted academic research access to Facebook (the social media platform) along with Instagram (a Facebook-owned social media platform). Instead, they have allowed access to sources where monetisation can easily occur, in particular, marketers and advertisers. This leaves academic researchers of digital social life in a difficult situation where API related research has been curtailed. In this paper we describe some rationales and methodologies for using APIs in social research. We then introduce some of the major events in academic API use that have led to the prohibitive situation researchers now find themselves in. Finally, we discuss the methodological and ethical issues this produces for researchers and, suggest some possible steps forward for API related research

Digital Inheritance in Web3: A Case Study of Soulbound Tokens and the Social Recovery Pallet within the Polkadot and Kusama Ecosystems

In recent years discussions centered around digital inheritance have increased among social media users and across blockchain ecosystems. As a result digital assets such as social media content cryptocurrencies and non-fungible tokens have become increasingly valuable and widespread, leading to the need for clear and secure mechanisms for transferring these assets upon the testators death or incapacitation. This study proposes a framework for digital inheritance using soulbound tokens and the social recovery pallet as a use case in the Polkadot and Kusama blockchain networks. The findings discussed within this study suggest that while soulbound tokens and the social recovery pallet offer a promising solution for creating a digital inheritance plan the findings also raise important considerations for testators digital executors and developers. While further research is needed to fully understand the potential impacts and risks of other technologies such as artificial intelligence and quantum computing this study provides a primer for users to begin planning a digital inheritance strategy and for developers to develop a more intuitive solution.Comment: To be published in IEEE Acces

Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation

Sharing Selves: Developing an Ethical Framework for Curating Social 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

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

The Disinformation Landscape and the Lockdown of Social Platforms

This introduction to the special issue considers how independent research on mis/disinformation campaigns can be conducted in a corporate environment hostile to academic research. We provide an overview of the disinformation landscape in the wake of the Facebook–Cambridge Analytica data scandal and social platforms’ decision to enforce access lockdowns and the throttling of Application Programming Interfaces (APIs) for data collection. We argue that the governance shift from user communities to social media algorithms, along with social platforms’ intensive emphasis on generating revenue from user data, has eroded the mutual trust of networked publics and opened the way for dis/misinformation campaigns. We discuss the importance of open, public APIs for academic research as well as the unique challenges of collecting social media data to study highly ephemeral mis/disinformation campaigns. The introduction concludes with an assessment of the growing data access gap that not only hinders research of public interest, but that may also preclude researchers from identifying meaningful research questions as activity on social platforms becomes increasingly more inscrutable and unobservable