The rapid expansion of renewable energy sources (RES) in many European countries brings about transmission grid expansion requirements. While the transition towards RESbased energy systems is largely perceived positively in general, locally both RES and grid expansion are often confronted with a lack of public acceptance. Using Germany as a case study, we analyse public acceptance of energy infrastructure and its main drivers on local vs. national levels. For this purpose, we conducted a nationally representative survey. Our results show that, on a national level, the acceptance of RES is very high and there is also a high acceptance of grid expansion if it helps to increase the share of RES in the system. In terms of local acceptance problems that may arise for most considered technologies, concerns about landscape modification turn out to be the main driving factor. Moreover, the distance between places of residence and places of energy infrastructure construction is crucial. While acceptance or rejection of technologies will never be entirely tangible or explicable, we find the explicability of rejections to be lowest for new technologies. Finally, age and education turn out to be the most relevant socio-demographic variables determining the participants' acceptance. Bertsch, V., Hall, M., Weinhardt, C., & Fichtner, W. (2016). Public acceptance and preferences related to -2 -renewable energy and grid expansion policy: Empirical insights for Germany. Energy, 114,[465][466][467][468][469][470][471][472][473][474][475][476][477]

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Across social media platforms users (sub)consciously represent themselves in a way which is appropriate for their intended audience. This has unknown impacts on studies with unobtrusive designs based on digital (social) platforms, and studies of contemporary social phenomena in online settings. A lack of appropriate methods to identify, control for, and mitigate the effects of self-representation, the propensity to express socially responding characteristics or self-censorship in digital settings, hinders the ability of researchers to confidently interpret and generalize their findings. This article proposes applying boosted regression modelling to fill this research gap. A case study of paid Amazon Mechanical Turk workers (n = 509) is presented where workers completed psychometric surveys and provided anonymized access to their Facebook timelines. Our research finds indicators of self-representation on Facebook, facilitating suggestions for its mitigation. We validate the use of LIWC for Facebook personality studies, as well as find discrepancies with extant literature about the use of LIWC-only approaches in unobtrusive designs. Using survey data and LIWC sentiment categories as predictors, the boosted regression model classified the Five Factor personality model with an average accuracy of 74.6%. The contribution of this work is an accurate prediction of psychometric information based on short, informal text.

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Googleþ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: ''zooming'' in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.

Face is the primary means of recognizing a person, transmitting information, communicating with others, and inferring people's feelings, among others. Our faces might disclose more than what we expect. A facial image can be informative of personal traits [1], such as race, gender, age, health, emotion, psychology, and profession. This study is triggered by Lombroso's research [2], which showed that criminals could be identified by their facial structure and emotions. While Lombroso's study looked at this issue from a physiology and psychiatry perspective, our study investigates whether or not machine learning algorithms would be able to learn and distinguish between criminal and non-criminal facial images. More specifically, we will look for gender biases in machine predictions. This is important because criminal facial images used to train

We aim to predict activities of political nature influencing or reflecting societal‐scale behavior and beliefs by applying learning algorithms to Twitter data. This study focuses on capturing domestic events in Egypt from November 2009 to November 2013. To this extent we study un­derlying communication patterns by evaluating content and metadata of 1.3 million tweets through computationally supported classification, with­out targeting specific keywords or users from the Twitter stream. Support Vector Machine (SVM) and Support Distribution Machine (SDM) classification algorithms are applied to detect and predict societal‐scale unrest. Latent Dirichlet Allocation (LDA) is used to create content‐based input patterns for the SVM while the SDM is used to classify sets of features created from meta‐data. The experiments reveal that user cen­tric approaches based on meta‐data outperform methods employing content‐based input despite the use of well established natural language pro­cessing algorithms. The results show that distri­butions over user centric meta information pro­vide an important signal when detecting and pre­dicting events. Applying this approach can assist policymakers and stakeholders in their efforts toward proactive community management.

There is an overriding interest in measuring the wellbeing of communities and institutions: healthy (flourishing) individuals and groups perform "better" than those that are not. Capturing the facets of well-being is, however, not straightforward: it contains personal information with sometimes uncomfortable self-realizations associated to it. Yet, the benefit of such data is the ability to observe and react to imbalances of a community, i.e. it can facilitate community management. Due to its personal nature, the observation of well-being needs to leverage carefully considered constructs. To have a comprehensive look at the concept of individual well-being, we propose a gamified frame of reference within a social network platform to lower traditional entrance barriers for data collection and encourage continued usage. In our setting, participants can record aspects of their well-being as a part of their "normal" social network activities, as well as view trends of themselves and their community. To evaluate the feasibility of our approach, we present the results of an initial study conducted via Facebook.

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