Enhancing Participation in Probability-Based Online Panels: Two Incentive Experiments and Their Effects on Response and Panel Recruitment

This article investigates how mail-based online panel recruitment can be facilitated through incentives. The analysis relies on two incentive experiments and their effects on panel recruitment, and the intermediate participation in the recruitment survey. The experiments were implemented in the context of the German Emigration and Remigration Panel Study and encompass two samples of randomly sampled persons. Tested incentives include a conditional lottery, conditional monetary incentives, and the combination of unconditional money-in-hand with conditional monetary incentives. For an encompassing evaluation of the link between incentives and panel recruitment, the article further assesses the incentives’ implications for demographic composition and panel recruitment unit costs. Multivariate analysis indicates that low combined incentives (€5/€5) or, where unconditional disbursement is unfeasible, high conditional incentives (€20) are most effective in enhancing panel participation. In terms of demographic bias, low combined incentives (€5/€5) and €10 conditional incentives are the favored options. The budget options from the perspective of panel recruitment include the lottery and the €10 conditional incentive which break-even at net sample sizes of 1000.

Digital Socialligators? Social Media-Induced Perceived Support During the Transition to the COVID-19 Lockdown

The sudden COVID-19-induced transition from a physical university life to a virtual one was a painful one for many students. Social distancing measures mean more than a simple change from face-to-face to online education. This study investigates how different social aspects, such as the students’ psychological sense of community, social capital, and use of social media, facilitated the perceived social support during the transition to the COVID-19 lockdown. Our results not only underline social media’s role, but also indicate that the perceived social support, as well as the bonding and bridging social capital, were particularly relevant during the transition process. Our findings are aimed at organizational management by recommending actionable ways in which they could improve social support by organizing computer-supported social networks, social support predictors, and specialized interventions for students with less perceived social support. As such, the study provides unique insights into the COVID-19-induced lockdown situation among students, while offering a transition model that also generalizes to other settings.

A Routine Activities Approach to Evidence-Based Risk Assessment: Findings From Two Simulated Phishing Attacks

To assess the efficacy of routine activity theory (RAT) for explaining phishing victimization and guide evidence-based policy, we launched two phishing attacks via a university Listserv ( N = 25,875). The first email offered access to a pdf file; the second offered free concert tickets. Several interesting findings emerged demonstrating phishing victimization results from network users’ routine behaviors. Students were significantly less likely to open the phishing email sharing a pdf but more likely to open the email offering free concert tickets. Moreover, students were mor e likely to click the malicious link embedded within the phishing email in both studies, often using mobile devices. Conversely, employees were more likely to click the link while connected to the university network, thus exposing the network to greater levels of risk. Finally, the email offering concert tickets was opened at a frequency more than double the email containing the pdf. Theoretical and policy implications are discussed.

Strategic Framing Matters But Varies: A Structural Topic Modeling Approach to Analyzing China’s Foreign Propaganda About the 2019 Hong Kong Protests on Twitter

Drawing upon the approach of strategic framing, this study investigated how China’s state-run media mobilize foreign propaganda machine and use specific patterns to describe the 2019 Hong Kong protests on Twitter. It also shed light on the heterogeneity of both production and reception of the strategic frames used by state media. Structural topic modeling was employed to analyze a large amount of Twitter content (i.e., 14,412 tweets) posted by 13 verified organizational accounts, and six strategic frames were identified as conflicts and violence, calling for stability and order, marginalizing protests, criticizing the West as accomplices, delegitimizing protests, and social and economic disruption. These frames highlighted insider–outsider and causes and consequences as two overarching communication strategies. The results also revealed that the bureaucratic rank of state media and the engagement rate of each tweet were closely associated with the content prevalence of various strategic frames. In addition to enhancing our understanding of the construction of “protest paradigm” against the social media context, these empirical findings uncover the often overlooked mobility and flexibility of China’s state media discourse as well as the communication ecology shaped and consolidated by the increasing importance state media communicators attach to online engagement metrics.

The Effects of Mobile Social Media Use on Older Migrants’ Social Integration and Life Satisfaction: Use Types and Self-Esteem Perspective

With the development of society and economy, more and more elderly people migrate from rural to urban areas. Nowadays, mobile social media (MSM) is a very important tool for information search, social communication, and entertainment among older migrants, and it is necessary to clear how the self-esteem level and the use types of MSM contribute to older migrants’ social integration and life satisfaction. Based on the literature review, we constructed a research model and tested the model using sample data of 306 Chinese older migrants. The results show that self-esteem has a negative impact on the social use and entertainment use of MSM. Our study also confirms that social use and entertainment use have different effects on older migrants’ social integration and life satisfaction. Social use has a positive impact on social integration and life satisfaction, while entertainment use has a negative impact on social integration and has no significant impact on life satisfaction. Furthermore, the perceived social integration of older adults can significantly predict their life satisfaction.

ei.Datasets: Real Data Sets for Assessing Ecological Inference Algorithms

Ecological inference models aim to infer individual-level relationships using aggregate data. They are routinely used to estimate voter transitions between elections, disclose split-ticket voting behaviors, or infer racial voting patterns in U.S. elections. A large number of procedures have been proposed in the literature to solve these problems; therefore, an assessment and comparison of them are overdue. The secret ballot however makes this a difficult endeavor since real individual data are usually not accessible. The most recent work on ecological inference has assessed methods using a very small number of data sets with ground truth, combined with artificial, simulated data. This article dramatically increases the number of real instances by presenting a unique database (available in the R package ei.Datasets) composed of data from more than 550 elections where the true inner-cell values of the global cross-classification tables are known. The article describes how the data sets are organized, details the data curation and data wrangling processes performed, and analyses the main features characterizing the different data sets.

Spillover as Movement Agenda Setting: Using Computational and Network Techniques for Improved Rare Event Identification

The increasing availability of data, along with sophisticated computational methods for analyzing them, presents researchers with new opportunities and challenges. In this article, we address both by describing computational and network methods that can be used to identify cases of rare phenomena. We evaluate each method’s relative utility in the identification of a specific rare phenomenon of interest to social movement researchers: the spillover of social movement claims from one movement to another. We identify and test five different approaches to detecting cases of spillover in the largest data set of protest events currently available, finding that an ensemble approach that combines clique and correspondence analysis and an ensemble approach combining all methods perform considerably better than others. Our approach is preferable to other ways of analyzing such cases; compared to qualitative approaches, our computational process identifies many more cases of spillover—some of which are surprising and would likely not be otherwise investigated. At the same time, compared to crude quantitative measures, our approach substantially reduces the “noise,” or identification of false-positive cases, of movement spillover. We argue that this technique, which can be adapted to other research topics, is a good illustration of how the thoughtful implementation of computational methods can allow for the efficient identification of rare events and also bridge deductive and inductive approaches to scientific inquiry.

Inside Trending Topic Algorithm: How Do Human Interactions Drive Public Opinion in an Artificial Environment

The object of this research is to exploit the algorithm of Twitter’s trending topic (TT) and identify the elements capable of guiding public opinion in the Italian panorama. The underlying hypotheses that guide the whole article, confirmed by the research results, concern the existence of (a) a limited number of elements at the base of each popular hashtag with very high viral power and (b) hashtags transversal to the themes detected by the Twitter algorithm that define specific opinion polls. Through computational techniques, it was possible to extract and process data sets from six specific hashtags highlighted by TT. In a first step through social network analysis, we analyzed the hashtag semantic network to identify the hashtags transversal to the six TTs. Subsequently, we selected for each data set the contents with high sharing power and created a “potential opinion leader” index to identify users with influencer characteristics. Finally, a cross section of social actors able to guide public opinion in the Twittersphere emerged from the intersection between potentially influential users and the viral contents.

The Impact of Parental Depression on Children’s Smartphone Addiction: A Serial Mediation Model With Parental Neglect and Children’s Self-Esteem

The present study investigates the mechanisms underlying the relationship between parental depression and children’s smartphone addiction. It explores the effects of parental depression on children’s smartphone addiction, as well as the mediating roles of parental neglect and children’s self-esteem in this relationship, which multiplies sequentially. We utilize data—comprising 2,396 children and their parents—from the National General Survey on Korean Children, using parent–child dyads. First, a hierarchical regression analysis shows that parental depression significantly and positively predicts children’s smartphone addiction ( B = .29, SE = .03, p < .001). Second, Hayes’s PROCESS macro (Model 6), executed to test the mediation effects, reveals that the effect of parental depression on children’s smartphone addiction is significantly mediated by parental neglect ( B = .07, Boot SE = .01, 95% Boot CI [.05, .10]) and children’s self-esteem ( B = .12, Boot SE = .01, 95% Boot CI [.10, .14]). Moreover, the serial mediation model’s results support that parental neglect and children’s self-esteem serially mediate the relationship between parental depression and children’s smartphone addiction ( B = .02, Boot SE = .004, 95% Boot CI [.01, .03]), implying that a higher level of parental depression is sequentially associated with increased parental neglect that reduces children’s self-esteem and consequently accelerates their smartphone dependence. The theoretical and practical implications of the results as well as the directions for future research are discussed.

Proximate or Primary Source? How Multiple Layers of News Sources on Social Media Predict News Influence

News shared on social media presents multiple layers of sources, from reputable news organizations to individual users who share news on social media. A web-based experiment investigated (a) whether the influence of a primary news source (news organization) on viewers decreases as it becomes less proximate with the presence of a more immediate source (individual user who shared news), and (b) if so, how the evaluations of both sources, along with a varying degree of issue relevance, affect viewers’ agreement with news position. Participants read one news article either shared on Facebook by a well-known celebrity or directly posted onto a news website. The perceived credibility of news organizations predicted viewers’ agreement with the news position, but only when the news was presented on a news web page so that the news organization was shown as the proximate source. When multiple sources were displayed, the influence of news organization credibility disappeared when the given news lacked personal relevance.