Suchergebnisse

Across social media platforms users (sub)consciously represent themselves in a way that 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 discusses a methodological approach to fill this research gap. A case study of paid Amazon Mechanical Turk respondents (n = 509) is presented where respondents completed psychometric surveys and provided fully anonymized access to their Facebook timelines. This case study concentrates on data collection and anonymization processes, and discusses issues in data harvested over the Internet.

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, Google+ 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.

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.

The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web‐based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.