Social Science Computer Review

In light of waning interpersonal contact in America, scholars have turned their attention to social network sites and the opportunities these provide for building and maintaining social relationships. The present study adds to this research, using national survey data from U.S. adults to examine how motivated use of Facebook for expanding and diversifying personal networks might revitalize real-world efforts of sociability for users, and returns to social capital that come by way of them. Results support our overall model relating weak-tie interactions to generalized trust. More importantly, when we use it to compare more and less sociable Facebook users, we find meaningful differences in the strength of the relationships between variables in the model. In particular, for more sociable users, interacting with weak ties was related to greater civic participation and increased feelings of generalized trust. Access to bridging social capital was a larger source of generalized trust for more sociable users as well. Ultimately, our findings suggest that sociability on sites like Facebook can facilitate the development of communication competence, where users who make a concerted effort of expanding and diversifying their personal networks come to interact with a wider array of individuals, and learn how to do so more effectively.

We examine the costs and benefits of nongovernmental organization (NGO) networking using a complex systems approach and agent-based modeling to simulate the effects of NGOs’ efforts to seek influence in policy making at home and abroad. We elaborate on the boomerang model developed by Keck and Sikkink and uncover macro-level effects of multiple NGOs networking for policy influence in multiple states around multiple positions on the same issue simultaneously. The results of our model and simulations lead us to argue that the boomerang effect has interesting unexplored implications for NGO behavior and state policy worthy of further empirical testing. We find that networking is necessary for NGOs to change state policy but leads to a higher likelihood of organizational collapse for NGOs. Although networking leads to policy change, as is well demonstrated within existing literature, our model suggests that efficacy comes at a cost to NGOs, which should make analysts and academics more ambivalent about the advisability of NGO networking.

In this article, we examine the relationship between metrics documenting politics-related Twitter activity with election results and trends in opinion polls. Various studies have proposed the possibility of inferring public opinion based on digital trace data collected on Twitter and even the possibility to predict election results based on aggregates of mentions of political actors. Yet, a systematic attempt at a validation of Twitter as an indicator for political support is lacking. In this article, building on social science methodology, we test the validity of the relationship between various Twitter-based metrics of public attention toward politics with election results and opinion polls. All indicators tested in this article suggest caution in the attempt to infer public opinion or predict election results based on Twitter messages. In all tested metrics, indicators based on Twitter mentions of political parties differed strongly from parties’ results in elections or opinion polls. This leads us to question the power of Twitter to infer levels of political support of political actors. Instead, Twitter appears to promise insights into temporal dynamics of public attention toward politics.

Surveys completed on mobile web devices (smartphones) have been found to take longer than surveys completed on a PC. This has been found both in surveys where respondents can choose which device they use and in surveys where respondents are randomly assigned to devices. A number of potential explanations have been offered for these findings, including (1) slower transmission over cellular or Wi-Fi networks, (2) the difficulty of reading questions and selecting responses on a small device, and (3) the increased mobility of mobile web users who have more distractions while answering web surveys. In a secondary analysis of student surveys, we find that only about one-fifth of the time difference can be accounted for by transmission time (between-page time) with the balance being within-page time differences. Using multilevel models, we explore possible page-level (question-level) and respondent-level factors that may contribute to the time difference. We find that much of the time difference can be accounted for by the additional scrolling required on mobile devices, especially for grid questions.

The number of respondents who access web surveys on a mobile device (smartphone or tablet) has been increasing rapidly over the last few years. Compared with desktop computers, mobile devices have smaller screens, different input options, and are used in a larger variety of locations and situations. The suspicion that the quality of data may suffer when online respondents use mobile devices has stimulated a growing body of research, which has mainly focused on paradata and web survey design. To investigate whether the respondents’ device affects the quality of web survey data, we examined the responses of 1,826 mobile-device and desktop participants in a political online survey that asked questions about the 2013 German federal election. To determine the reliability and validity of data submitted via mobile devices, we determined the consistency of the participants’ responses across questions and validated the responses against various internal and external criteria. Replicating previous findings, mobile-device respondents were younger and more likely to be female, and they produced higher dropout rates and longer completion times than desktop respondents. However, data produced by respondents using mobile devices were as consistent, reliable, and valid as data produced by respondents using desktop computers. These findings contradict the notion that mobile-device users compromise the reliability and validity of data collected online and suggest that researchers do not necessarily need to be afraid of the participation of mobile-device respondents in web surveys.

A key goal of survey interviews is to collect the highest quality data possible from respondents. In practice, however, it can be difficult to achieve this goal because respondents do not always understand particular survey questions as designers intended. Researchers have used a variety of indicators to identify and predict respondent confusion and difficulty in answering questions in different modes. In web surveys, it is possible to automatically detect response difficulty in real time. The research to date has focused on response latencies—mostly long response times—as evidence of difficulty. In addition to response latencies, however, web surveys offer rich behavioral data, which may predict respondent confusion and difficulty more directly than response times. This article focuses on one such behavior, mouse movements. We examine a set of mouse movements participants engage in when answering questions about experimental scenarios whose difficulty has been manipulated (as confirmed by respondent ratings). This approach makes it possible to determine which movements are general movements, demonstrating how a person interacts with a computer, and which movements are related to response difficulty. We find not only that certain mouse movements are highly predictive of difficulty but also that such movements add considerable value when used in conjunction with response times. The approach developed in this article may be useful in delivering help to confused respondents in real time and as a diagnostic tool to identify confusing questions.

Recent innovations in conflict and computer research favor generating massive event data using automated coding protocols. Unfortunately, these approaches almost exclusively rely on English-language sources, thus causing problems of coverage bias and misleading inferences. In an effort to attenuate Anglocentrism in event data, we introduce Eventus ID, new software for supervised event coding from text written in Spanish. Drawing on real news reports, the application generates daily georeferenced data on how the military fights organized criminals in Mexico. Performance metrics show that Eventus ID is almost as accurate as humans for coding event data.

This article presents a continuous measure of future self-continuity (FSC-C) designed for use in web-based surveys. It allows researchers to assess on a continuous scale the similarity or connectedness that participants feel in relation to their future selves. The measure has an intuitive drag-and-drop interface, where participants can drag one circle over another circle to a certain degree of overlap to indicate closeness of the relation between their present self and their future selves. The measure is highly customizable and is therefore also attractive for researchers in other domains (e.g., to measure Inclusion of Other in the Self). In this regard, the measure is an alternative to that reported by Le, Moss, and Mashek in this journal. This article describes the motivation for the development of the measure as well as how it is constructed.