Oliver Posegga

Digital Trace Data in the Study of Public Opinion : An Indicator of Attention Toward Politics Rather Than Political Support

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.

Internal crowdsourcing: conceptual framework, structured review, and research agenda

The use of IT-enabled crowdsourcing with employees in enterprises has increased substantially in recent years. This phenomenon, which we refer to as ‘internal crowdsourcing’, is distinct both from external crowdsourcing with end users and from hierarchy-based work with employees. A literature stream has emerged that corresponds with the increased relevance of internal crowdsourcing in practice. The purpose of this review paper of internal crowdsourcing is to provide conceptual development, synthesise the literature, and provide a research agenda. In the review reported in this paper, we systematically analysed and critically reviewed the literature in this domain published thus far (74 papers). We found useful findings and insights into a new and relevant IT-enabled phenomenon. At the same time, we also found conflicting definitions and conceptualisation, as well as research efforts that are not well integrated. The paper supports future research on internal crowdsourcing by providing improved conceptualisation, consolidating insights, and identifying important areas for future research.

Towards a Conceptualization of Data and Information Quality in Social Information Systems

Data and information quality (DIQ) have been defined traditionally in an organizational context and with respect to traditional information systems (IS). Numerous frameworks have been developed to operationalize traditional DIQ accordingly. However, over the last decade, social information systems (SocIS) such as social media have emerged that enable social interaction and open collaboration of voluntary prosumers, rather than supporting specific tasks as do traditional IS in organizations. Based on a systematic literature review, the paper identifies and categorizes prevalent DIQ conceptualizations. The authors differentiate the various understandings of DIQ in light of the unique characteristics of SocIS and conclude that they do not capture DIQ in SocIS well, nor how it is defined, maintained, and improved through social interaction. The paper proposes a new conceptualization of DIQ in SocIS that can explain the interplay of existing conceptualizations and provides the foundation for future research on DIQ in SocIS.

Using Weighted Interaction Metrics for Link Prediction in a Large Online Social Network

There has been a considerable amount of recent research on the link prediction problem, that is, the problem of accurately predicting edges that will be established between actors in a social network in a future period. With the cooperation of the provider of a German social network site (SNS), we aim to contribute to this line of research by analyzing the link formation and interaction patterns of approximately 9.38 million members of one of the largest German online social networks (OSN). It is our goal to explore the value of users’ interaction frequencies for link prediction based on metrics of local structural similarity. Analyzing a random sample of the network, we found that only a portion of the network is responsible for most of the activity observed: 42.64 % of the network’s population account for all observed interactions and 25.33 % are responsible for all private communication. We have also established that the degree of recent interaction is positively correlated with imminent link formation – users with high interaction frequencies are more likely to establish new friendships. The evaluation of our link prediction approach yields results that are consistent with comparable studies. Traditional metrics seem to outperform weighted metrics that account for interaction frequencies. We conclude that while weighted metrics tend to predict strong ties, users of SNS establish both strong and weak ties. Our findings indicate that members of an SNS prefer quantity over quality in terms of establishing new connections. In our case, this causes the simplest metrics to perform best.

Understanding the lifestyle of health and sustainability – an exploratory study

The goal of this paper is to study the psychographic and demographic attributes and the interests of lifestyle of health and sustainability (LOHAS) consumers. Based on a dataset of 3,813 user profiles from the largest LOHAS community site on Facebook, we applied association rule mining and logistic regression to achieve this goal. We focused our work on applying the collaborative innovation network (COIN) concept to the LOHAS consumer. Results show that LOHAS consumers have a strong spiritual attitude and a general interest in outdoor and fitness activities such as hiking, gardening, and yoga. Finally, we provide a description of a LOHAS consumer profile archetype and compare our findings to existing work.