Margeret Hall

How do politicians use Facebook? An applied Social Observatory

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for todays 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.

Towards the gamification of well-being measures

There is an overriding interest in measuring the well-being 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.

BeWell: A Sentiment Aggregator for Proactive Community Management

Granular, localized information can be unobtrusively gathered to assess public sentiment as a superior measure of policy impact. This information is already abundant and available via Online Social Media. The missing link is a rigorous, anonymized and open source artefact that gives feedback to stakeholders and constituents. To address this, BeWell, an unobtrusive, low latency multi-resolution measurement for the observation, analysis and modelling of community dynamics, is proposed. To assess communal well-being, 42 Facebook pages of a large public university in Germany are analyzed with a dictionary-based text analytics program, LIWC. We establish the baseline of emotive discourse across the sample, and detect significant campus-wide events in this proof of concept implementation, then discuss future iterations including a community dashboard and a participatory management plan.

Well-Being's predictive value: a gamified approach to managing smart communities

Well-being is a multifaceted concept, having intellectual origins in philosophy, psychology, economics, political science, and other disciplines. Its presence is correlated with a variety of institutional and business critical indicators. To date, methods to assess well-being are performed infrequently and superficially; resulting in highly aggregated observations. In this paper, we present well-being as a predictive entity for the management of a smart community. Our vision is a low latency method for the observation and measurement of well-being within a community or institution that enables different resolutions of data, e.g. at the level of an individual, a social or demographic group, or an institution. Using well-being in this manner enables realistic, faster and less expensive data collection in a smart system. However, as the data needed for assessing well-being is highly sensitive personal information, constituents require incentives and familiar settings to reveal this information, which we establish with Facebook and gamification. To evaluate the predictive value of well-being, we conducted a series of surveys to observe different self-reported psychological aspects of participants. Our key findings were that neuroticism and extroversion seem to have the highest predictive value of self-reported well-being levels. This information can be used to create expected trends of well-being for smart community management.

Measuring Your Best You: A Gamification Framework for Well-Being Measurement

Well-being is a multifaceted concept, having intellectual origins in philosophy, psychology, economics, political science, and other disciplines. To date, methods to assess well-being are performed infrequently (e.g. yearly) and superficially, resulting in highly aggregated observations. Our objective is to create a measurement framework for assessing (human) well-being with a much higher observation frequency (e.g. daily). In decreasing the time between observations of well-being, we believe that better processes for the management of social groups and communities (e.g. a workforce) can be implemented. In this paper, we discuss our experiences and findings from the implementation of a gamified Facebook application for the measurement of well-being. We pay special attention to the accuracy of well-being observations, and the efficacy of various gamification incentives on continued use by users. Our results show that gamification provides a suitable environment for extracting accelerated, realistic, truthful self-reporting for the measurement of human flourishing.

Am I who I say I am? Unobtrusive self-representation and personality recognition on Facebook

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.

Editorial of the Special Issue on Following User Pathways: Key Contributions and Future Directions in Cross-Platform Social Media Research

ABSTRACT Social media and the resulting tidal wave of the available data have changed the ways and methods researchers analyze communities at scale. But the full potential for social scientists (and others) is not yet achieved. Despite the popularity of social media analysis in the past decade, few researchers invest in cross-platform analyses. This is a major oversight as a majority of online social media users have multiple social media accounts. Missing are the models and tools necessary to undertake analysis at scale across multiple platforms. Especially promising in support of cross-platform analysis is the mixed-method approach (e.g., qualitative and quantitative methods) in order to better understand how users and society interact online. This special issue “Following User Pathways” addresses methodological, analytical, conceptual, and technological challenges and opportunities of cross-platform analysis in social media ecosystems.

Tweet if you will: the real question is, who do you influence?

Large numbers of todays businesses use social media in advertising. There is a belief in a great opportunity, even if return on investment is difficult to quantify. To fill this gap we consider a cross-media-platform-analysis across Facebook, Twitter and Foursquare. Rationale for and against different characteristics within social media advertisement are addressed. The paper finds correlation from posts and tweets to Foursquare check-ins. Results show that posts or tweets containing pictures have higher return on investment than posts or tweets without, and that when the text of a post or tweet raises curiosity or attracts individuals or groups Foursquare check-ins increase.

Combining Agile Software Development and Service-learning: A Case Study in Experiential IS Education

Experiential learning is ever-more popular with educators, industry, and with students themselves. Finding and delivering appropriate applied use cases can be challenging though, as on one hand industry partners may not willing to give insights to non-employees into their systems for creating truly meaningful case studies, and on the other hand the appropriate balance between instruction and application is ill-defined. Service learning projects are one solution for filling in the applied project gap. This case study takes place in the nexus between blended classrooms, applied software development, and service learning. Junior and senior level students partnered with a community actor to develop deployable software applying the Agile methodology. The service-learning project enabled students to engage in a full-cycle development project, from requirements gathering to hypercare. However, significant trade-offs in structure and classroom management must be made when the focus of the class is a full implementation. Blended technologies and course delivery were found to aid delivery and project management in a seamless manner. Drawing on feedback from stakeholders and students, this experience report makes a series of recommendations for implementing applied software development. Our contribution is the introduction and assessment of a method to marry (online) information systems education with service learning.

Micro-internships on the Margins.

Mooqita creates, delivers, and proctors a massively open online course (MOOC) in conjunction with a regional homeless shelter and local industry for people currently experiencing homelessness. It is estimated that 25% of the homeless are working or have worked within the past quarter. Additionally, around 30% of homeless in the metro area have previously served in the armed forces. Some number of the homeless are employed or employable: still, these numbers are an indicator that the local homeless population is suffering from a basic mismatch of skills and the local labor market. Mooqita provides an ideal match because local companies face a shortage of workers, while potential employees struggle with a variety of challenges that may interfere with gaining the appropriate training and experience required by those employers. By designing the program specifically to target this population, we meet their needs for training and valuable job skills in a uniquely challenging situation. The self-paced course will concentrate on three locally in-demand technology skills. The value of this approach to learners is three-fold. Learners will learn by doing with practical, near-to-real world challenges. They will graduate from the MOOC with in-demand skillsets. Finally, tying learning outcomes to small payments incentivizes course completion while gaining financial stability. Local industry will also benefit from a stronger workforce pipeline trained to their specific needs.