Digital methods in a post-API environment

ABSTRACT

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.     

News and information leadership in the digital age

This paper examines information networks on social media to draw conclusions about influence relationships among members of the mass media. The project considers social networks and information patterns using Twitter data, first at the newspaper level and second at the journalist level. Using a computational approach, we look for evidence of elite-directed information flows, as well as exploring whether we find evidence of an increase in the democratization of newsmaking. This study finds that elite voices continue to dominate information networks in the digital age; however, it also finds evidence that information can move expeditiously from journalists in local and regional outlets to elite ones, and vice versa. We move further to explore the content of tweets among the journalist network, finding that there are substantial, direct interactions among elite and regional and local journalists. Our results taken together uncover new network patterns and provide a novel insight on the role of information technologies in newsmaking in the digital age.     

How do public organizations communicate their alliances on Facebook and how does the public react? A longitudinal analysis

Organizations have adopted social media to communicate their alliances to the public, that is, engage in representational networks. Yet a comprehensive examination of what is communicated in representational networks and how public reactions are associated with these networks is relatively scarce. This study builds on the Symbiotic Sustainability Model (SSM) to examine the content of messages passed in representational networks on social media and their related public reactions. Based on a panel analysis of one year of Facebook data generated by 605 public organizations in Taiwan, this study reveals that referencing certain topics such as economic and technological development and employing linguistic features demonstrating efficacy, formality, and empathetic concern in Facebook messages was associated with an increased diversity of alliance ties reported by public organizations. Moreover, the relationship between communicating diverse alliance ties and positive public reactions in the form of likes went both ways. Integrating automated topic modeling, linguistic analysis, and panel analysis, this study provides a process-based and communication-centered view of the mechanisms and disparities underlying organizations’ strategic network management on social media.     

Question-order effects in social network name generators

Social network surveys are an important tool for empirical research in a variety of fields, including the study of social capital and the evaluation of educational and social policy. A growing body of methodological research sheds light on the validity and reliability of social network survey data regarding a single relation, but much less attention has been paid to the measurement of multiplex networks and the validity of comparisons among criterion relations. In this paper, we identify ways that surveys designed to collect multiplex social network data might be vulnerable to question-order effects. We then test several hypotheses using a split-ballot experiment embedded in an online multiple name generator survey of teachers’ advice networks, collected for a study of complete networks. We conclude by discussing implications for the design of multiple name generator social network surveys.     

Dynamic social media affiliations among UK politicians

Inter-personal affiliations and coalitions are an important part of politicians’ behaviour, but are often difficult to observe. Since an increasing amount of political communication now occurs online, data from online interactions may offer a new toolkit to study ties between politicians; however, the methods by which robust insights can be derived from online data require further development, especially around the dynamics of political social networks. We develop a novel method for tracking the evolution of community structures, referred to as ‘multiplex community affiliation clustering’ (MCAC), and use it to study the online social networks of Members of Parliament (MPs) and Members of the European Parliament (MEPs) in the United Kingdom. Social interaction networks are derived from social media (Twitter) communication over an eventful 17-month period spanning the UK General Election in 2015 and the UK Referendum on membership of the European Union in 2016. We find that the social network structure linking MPs and MEPs evolves over time, with distinct communities forming and re-forming, driven by party affiliations and political events. Without including any information about time in our model, we nevertheless find that the evolving social network structure shows multiple persistent and recurring states of affiliation between politicians, which align with content states derived from topic analysis of tweet text. These findings show that the dominant state of partisan segregation can be challenged by major political events, ideology, and intra-party tension that transcend party affiliations.     

Assessing the bias in samples of large online networks

We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the search and the streaming APIs, and to different filtering parameters. We find that smaller samples do not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions, partly because of the higher influence of snowballing in identifying relevant nodes. We discuss the implications of this bias for the study of diffusion dynamics and political communication through social media, and advocate the need for more uniform sampling procedures to study online communication.     

The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic,multidimensional social network data

The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N₁ = 226, N₂ = 261, N₃ = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition.     

Whose results are these anyway? Reciprocity and the ethics of “giving back” after social network research

Bridging the social networks, field methods and ethics literatures, I make the case that the process of reporting research findings is an ethical issue, and recommend elevating it in the research design. I draw on a reflective account of three research experiences with settings in, respectively, online health communities, economic organizations, and the mainstream media. I proceed in steps, discussing release of personal network results to individual participants, of whole network results to the researched community, and finally of general results to wider audiences, under a unifying idea that a reciprocity obligation underlies the reporting process. I claim that communication should follow an iterative rather than a linear approach to reach all relevant stakeholders, thereby mitigating the vulnerabilities that arise from research.     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.     

Network Instability in Times of Stability

Personal networks undergo change in response to major life course events. Individual, relational, and network characteristics that influence network instability in the absence of a significant life transition/crisis are less understood. We focus on those ties that transition from active to dormant. Because the shift to dormancy is often interpreted as a reduction in support or social capital, it is considered problematic. This study is based on longitudinal survey data of middle‐class adults who did not undergo life changes. Even in this context of relative stability, support networks experience rates of dormancy similar to those observed during periods of major upheaval. Tie dormancy is unrelated to individual characteristics, network size and density, or homophily along dimensions other than sex. Frequency and medium of communication are particularly notable as factors that were not related to tie dormancy. Ties were less likely to become dormant if they were geographically or emotionally close, immediate kin or neighbors, highly supportive, the same sex, or more embedded in the network. These findings provide context for how support networks operate when not buffeted by exogenous forces. They provide a baseline for understanding the impact on networks of transitions, trauma, new media, and difficult life circumstances.     

From attitudes to social networks: National gender-role attitudes and gender differences in late-life social relationships

Studies often find gender differences in social networks in later life, but are these findings universal, or do they differ in various cultural contexts? To address this research gap, the current study examines the association between gender differences in social relationships and country-level gender-role attitudes. We combined data from the Survey of Health, Ageing and Retirement in Europe (SHARE) of individuals aged 50 years and older with country-level data on gender-role attitudes from the European Values Survey (EVS) for 15 European countries. We estimated a series of multivariate hierarchical regression models that predicted the size of the personal social network, its emotional closeness, and the proportion of the spouse, children, and friends in the network. The results indicated gender differences in social network characteristics. Women reported larger social networks and were more likely to have larger proportions of children and friends but smaller proportions of the spouse in their social networks. The magnitude of gender differences was associated with country-level gender-role attitudes. In countries with more egalitarian gender-role attitudes, women had larger networks with a larger proportion of friends compared to men. In countries with more traditional gender-role attitudes, women had larger proportions of their children and spouse in their social networks and had emotionally closer networks. Our findings suggest that the societal context and opportunity structures for social interactions play an important role in shaping the structure of women’s and men’s social relationships in later life.     

Polarized public opinion responding to corporate social advocacy: Social network analysis of boycotters and advocators

Despite growing attention to corporate social-political advocacy, little is known about how publics mobilize and establish relationships in social media when firms are involved in hot-button issues. Using the social network approach, this study examines a network structure which emerged around boycotting and advocating for Starbucks and Budweiser when these two brands responded to President Donald Trump’s immigration ban executive order in 2017. The study identified three unique characteristics in the boycotters’ networks. The boycotters appeared not only in the aggregated brand boycotting networks, but also in the advocators’ networks. In addition, boycotters in Budweiser and Starbucks networks were engaged in boycotting other brands or organizations which were opposed to Republicans or President Trump’s policy. Finally, the network of boycotters was very dense and highly connected among subgroups while that of advocators was sparse. Theoretical and practical implications are discussed.     

A comparison of email networks and off-line social networks: A study of a medium-sized bank

Recently there has been a surge in the availability of online data concerning the connections between people, and these online data are now widely used to map the social structure of communities. There has been little research, however, on how these new types of relational data correspond to classical measures of social networks. To fill this gap, we contrast the structure of an email network with the underlying friendship, communication, and advice seeking networks. Our study is an explorative case study of a bank, and our data contains emails among employees and a survey of the ego networks of the employees. Through calculating correlations with QAP standard errors and estimating exponential random graph (ERG) models, we find that although the email network is related to the survey-based social networks, email networks are also significantly different: while off-line social networks are strongly shaped by gender, tenure, and hierarchical boundaries, the role of these boundaries are much weaker in the email network.     

Network effects on organizational decision-making: Blended social mechanisms and IPO withdrawal

This paper develops a new approach to the study of network effects in organizations and markets by proposing that structural influences on social and economic action result from contingent blends of well-understood social mechanisms. We emphasize the interplay of three different network processes: resource and information transfer, status signaling and certification, and social influence. Different mixes of these mechanisms characterize disparate networks because the obligations imposed by ties and the capacities of partners result in situations where mechanisms amplify or diminish one another. We test hypotheses about mechanism interactions using four years (1997–2000) of data on high-technology IPOs that situate organizational decisions about whether to withdraw an offering in two distinct networks. We find that network mechanisms exert multiple moderating effects on one another and that those effects vary systematically across venture capital syndicate and director interlock networks. These findings help to explain why different networks exert disparate effects, why the effects of some structures change as their larger contexts shift, and why even very successful organizations can sometimes find themselves hamstrung by their connections.     

Resolving complex community problems: Applying collective leadership and Kotter's change model to wicked problems within social system networks

How does collective leadership within social networks resolve chronic and complex problems common to communities? Unfortunately, sometimes it does not, but when it does, the outcome may be truly extraordinary. We use a case study approach to explain how one Midwest community within the USA applied collective leadership within a community network to reduce teen births. It took ten years of what many identified as provocative media campaigns and comprehensive sex education programs to reduce teen births by 65%, significantly exceeding the stated goal. Using Kotter's change model as a backdrop, powerful strategies and provocative creativity reveal courageous leadership within a social network of diverse people and organizations focused on improving the social well being of their community.     

Cluster analysis of multiplex networks: Defining composite network measures

Social relations are multiplex by nature: actors in a group are tied together by various types of relationships. To understand and explain group processes it is, therefore, important to study multiple social networks simultaneously in a given group. However, with multiplexity the complexity of data also increases. Although some multivariate network methods (e.g. Exponential Random Graph Models, Stochastic Actor-oriented Models) allow to jointly analyze multiple networks, modeling becomes complicated when it focuses on more than a few (2–4) network dimensions. In such cases, dimension reduction methods are called for to obtain a manageable set of variables. Drawing on existing statistical methods and measures, we propose a procedure to reduce the dimensions of multiplex network data measured in multiple groups. We achieve this by clustering the networks using their pairwise similarities, and constructing composite network measures as combinations of the networks in each resulting cluster. The procedure is demonstrated on a dataset of 21 interpersonal network dimensions in 18 Hungarian high-school classrooms. The results indicate that the network items organize into three well-interpretable clusters: positive, negative, and social role attributions. We show that the composite networks defined on these three relationship groups overlap but do not fully coincide with the network measures most often used in adolescent research, such as friendship and dislike.     

Social Media and Social Work: The Challenges of a New Ethical Space

ABSTRACT

Social media and other online technologies have transformed communication between social workers and service users, with many practitioners engaging and working with clients through social networking sites. While there are numerous ethical issues associated with online practice related to con?dentiality, dual relationships, and boundary crossing, there is lack of clarity about how to deal with such issues. This article uses a case example to develop a nuanced understanding of ethical issues and ethical behaviour in online spaces. We argue that social workers need to link their knowledge of the complex interplay between discourses that underpin daily practice like those related to power, permanency, authorship, audience, embodiment, and professionalism to social media created spaces. In doing this, social workers must retain their commitment to ethical values and critical reflective practice. We conclude with recommendations for education, research, and practice.     

Fostering social capital in an international multi-stakeholder issue network

Scholars have theorized that public relations contributes to societies and communities by bringing attention to pressing public issues and fostering social capital in civil society networks. However, the extant research has studied civil society networks of NGOs, donors, and the media in transitional countries. This study extends the public relations model of civil society in two ways. First, it broadens the scope to an international context. Second, it draws from the multi-stakeholder issue network perspective to conceptualize a civil society network as a space where stakeholders of an issue mix their interests as they collectively address a pressing public issue. The literature on international and multi-stakeholder networks suggest that the international scope and the mixing of interests across sectors may restrict the production of social capital. The results from the social network analysis suggests that the mixing of interests across sectoral and geopolitical boundaries did not restrict the production of social capital. Rather, the patterns of the relationships among those on the core and those on the periphery of the network restricted the production of social capital. Such finding demonstrates how public relations’ functions like relationship building can have profound influences on social capital and civil society networks. The implications for public relations theorizing and research are discussed.     

Logit Models for Affiliation Networks

Once confined to networks in which dyads could be reasonably assumed to be independent, the statistical analysis of network data has blossomed in recent years. New modeling and estimation strategies have made it possible to propose and evaluate very complex structures of dependency between and among ties in social networks. These advances have focused exclusively on one-mode networks—that is, networks of direct ties between actors. We generalize these models to affiliation networks, networks in which actors are tied to each other only indirectly through belonging to some group or event. We formulate models that allow us to study the (log) odds of an actor's belonging to an event (or an event including an actor) as a function of properties of the two-mode network of actors' memberships in events. We also provide illustrative analysis of some classic data sets on affiliation networks.