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.     

Health science libraries in Sweden: new directions,expanding roles

Librarians in Sweden are facing huge challenges in meeting the demands of their organisations and users. This article looks at four key areas: coping with open science/open access initiatives; increasing demands from researchers for support doing systematic reviews; understanding user experiences in Swedish health science libraries; and the consequences of expanding roles for recruitment and continuing professional development. With regard to changing roles, there is an increasing shift from the generalist towards the expert role. The authors raise the issue as to how to prepare those new to the profession to the changing environment of health science libraries.     

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.     

The Formation of Swedish Media Studies, 1960–1980

Around 1960, the politics of the emerging media society in Sweden tended to fixate the formative functions of mass communication. The monopoly of public service broadcast media, press subsidies and new tendencies in film policy were some of the issues around which uncertainty prevailed. New methods to provide reliable data were sought by politicians, since empirical facts were required as arguments for an updated media policy. This article examines the different ways that the field of media studies was introduced in Sweden between 1960 and 1980. We argue that Swedish academic media studies departed from, and emerged within, a rather diffuse borderland between industry, politics and academia. The formation of national media research in Sweden can partly be seen as an effect of politicians and the media industry wanting to be better informed on issues such as media influence, media ownership and the habits and composition of the media audience.     

Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology

ABSTRACT

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.     

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.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.     

Placing Facebook

Facebook is challenging professional journalism. These challenges were evident in three incidents from 2016: the allegation that Facebook privileged progressive-leaning news on its trending feature; Facebook’s removal of the Pulitzer Prize-winning “Napalm Girl” photo from the pages of prominent users; and the proliferation of “fake news” during the US presidential election. Using theoretical concepts from the field of boundary work, this paper examines how The Guardian, The New York Times, Columbia Journalism Review and Poynter editorialized Facebook’s role in these three incidents to discursively construct the boundary between the value of professional journalism to democracy and Facebook’s ascendant role in facilitating essential democratic functions. Findings reveal that these publications attempted to define Facebook as a news organization (i.e., include it within the boundaries of journalism) so that they could then criticize the company for not following duties traditionally incumbent upon news organizations (i.e., place it outside the boundaries of journalism). This paper advances scholarship that focuses on both inward and outward conceptions of boundary work, further explores the complex challenge of defining who a journalist is in the face of rapidly changing technological norms, and advances scholarship in the field of media ethics that positions ethical analysis at the institutional level.