Institutional policy process improvement: using structurating activity theory to inform practice

Organizational communication scholars are increasingly called upon to use theoretical knowledge to address problematic processes in public institutions. This article reports a process improvement project focused on a university conflict of interest (COI) policy as an example of how scholars can engage theory to improve ongoing policy processes. The project team included communication scholars and research administration staff who designed, implemented, and evaluated the mixed-methods project guided by structurating activity theory. Baseline survey results were used to design the process improvement project. Results of the follow-up survey indicate that researchers who participated in some aspect of the process improvement project (i.e. attending a policy café, using the redesigned website, or reading the new Quick Reference Guide) reported higher levels of policy communication and knowledge and more positive attitudes toward the COI policy than researchers who had not engaged with process improvement efforts. We offer theoretical and practical implications of results for organizational communication scholars and practitioners.     

Russian Science Citation Index: Areas for Development Identified by Active Users

The current state of the Russian Science Citation Index is analyzed. Several areas for its further development identified by long-standing active users, such as data searching, refinement, and storage; current user alerts; data input with the Science Index license; patent information; and the application programming interface (API) are investigated.     

Why Users Share the News: A Theory of Reasoned Action-Based Study on the Antecedents of News-Sharing Behavior

Social media have become an integral part of online news use, affecting how individuals find, consume, and share news. By applying the Theory of Reasoned Action (TRA), this study investigates the effects of motives, attitude, and intention on news-sharing behavior among German social media users (n = 333). Findings show that news-sharing attitude and subjective norms have a positive effect on news-sharing intention, which in turn has a positive effect on actual news-sharing behavior. Taken together, we see that a new media behavior in the early phases of its societal diffusion—like social media news sharing in Germany in 2015—can mainly be explained by a rational choice logic and is rooted in the motives of socializing and information seeking. This finding thus reflects the double nature of social media as a means for both information retrieval and social grooming.     

Reasons for Student Engagement in Extra-Class Communication

This study extends instructional communication research on extra-class communication (ECC) by presenting an integrative analysis comparing the explanatory utility of student- and instructor-oriented reasons for student engagement in frequent ECC. Results from 495 college students suggested that student-oriented reasons, specifically the relational and functional student motives, were the only significant, positive predictors of students’ perceptions of frequent ECC engagement. Other student motives (excuse making, participation, sycophancy) and perceptions of instructor approachability did not significantly predict perceived frequency of ECC engagement.     

Out of Sight, (Not Yet) Out of Mind: The Impact of Tie Strength on Direct Interaction and Social Surveillance Among Geographically Close and Long-Distance Facebook Friends

Social networking sites are considered a valuable resource to maintain existing friendships even over considerable distances. The present study emphasizes tie strength as a crucial determinant for the use of interactive and passive features among 302 active Facebook users and friends of different relational closeness (close friends vs. casual friends vs. acquaintances) as well as a moderator for the impact of physical distance. As expected, tie strength affected direct interaction linearly, whereas social surveillance followed a quadratic trend. Furthermore, moderation analyses revealed less frequent direct interaction between close friends and stronger surveillance of casual friends’ profiles as physical distance increases. These results indicate different functions in relationship maintenance for both behaviors as direct interaction aims at nurturing current relationships, while surveillance serves as a catalyst for promising future communications. As a consequence, physical distance influences direct interaction adversely due to emerging constraints but also contains informational value encouraging information-seeking behaviors.     

Alexa,Siri, Cortana,and More: An Introduction to Voice Assistants

Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today’s voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

From Archive,to Access,to Experience––Historical Documents as a Basis for Immersive Experiences

Libraries have been the key to preserving culture and historic legacy for centuries. One such treasure cataloged in The Pennsylvania State University (Penn State) Libraries is a collection of over 33,000 Sanborn? Fire Insurance Maps. Originally kept safe in metal drawers, the library has embarked on a journey to digitize this abundance of information, combine it with other media such as photographs, and make it accessible through a web interface. Inspired by these efforts, we accessed this information and took it to the next level. Using state of the art 3D modeling and immersive technologies, we created a historic 3D model and immersive experiences of Penn State, exemplarily for the 1922 campus. The resulting experiences can be accessed through the web but also through head mounted displays (HMDs) and mobile phones in combination with VR viewers such as the Google Cardboard. Additionally, they can be used anywhere in the world or on the campus itself as a way to enable remote and in situ experiences and learning. Immersive experiences let us connect to the past, the present and the future, and as such offer value to digital cultural heritage efforts.     

Scaling up Content Analysis

ABSTRACT

Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.