Dynamic network analytics for recommending scientific collaborators

Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field

COMMUNITY DETECTION IN GRAPHS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well

The Datafied Society. Studying Culture through Data

As more and more aspects of everyday life are turned into machine-readable data, researchers are provided with rich resources for researching society. The novel methods and innovative tools to work with this data not only require new knowledge and skills, but also raise issues concerning the practices of investigation and publication. This book critically reflects on the role of data in academia and society and challenges overly optimistic expectations considering data practices as means for understanding social reality. It introduces its readers to the practices and methods for data analysis and visualization and raises questions not only about the politics of data tools, but also about the ethics in collecting, sifting through data, and presenting data research. AUP S17 Catalogue text As machine-readable data comes to play an increasingly important role in everyday life, researchers find themselves with rich resources for studying society. The novel methods and tools needed to work with such data require not only new knowledge and skills, but also a new way of thinking about best research practices. This book critically reflects on the role and usefulness of big data, challenging overly optimistic expectations about what such information can reveal, introducing practices and methods for its analysis and visualization, and raising important political and ethical questions regarding its collection, handling, and presentation

Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis

Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis

If this stuff matters, why isn\u27t it being shared? : citations, hyperlinks, and potential public futures of online writing in rhetoric and composition.

This dissertation addresses two deceptively discrete questions: (1) how academics might reach wider public audiences, and (2) how and why people cite the way they do. It takes citation practices as a telling though often tacit practice, one through which it is possible trace the contours of a larger story about how writing is changing as it moves online. That story: Writers increasingly reflect goals of provocation, of attracting a wider and potentially global audience, of spreading a message rapidly and virally, of responding to recent events and conversations, of sharing sources and resources. To explore these questions, this dissertation forwards a mixed-methods study of citation and writing practices in three different sites: In popular press web writing (on Slate and Newsweek—Chapter II), in traditional academic print text in rhetoric and composition (in CCC and College English—Chapter III), and in academic webtext online (on Kairos and Computers and Composition Online—Chapter IV). Chapter II conducts a rhetorical corpus analysis of Slate and Newsweek, seeking transcendent citation practices within each journal and considering how those practices (and other writing practices) and others correlate (or not) with social sharing; I then report on interviews with authors from Slate, aiming to elucidate those findings. Chapter III conducts a rhetorical corpus analysis of CCC and College English, seeking an understanding of citation practices in the field of rhetoric and composition more traditionally, more historically; as in the previous chapter, these findings are commented upon and elucidated by authors/editors of each journal. Chapter IV considers hyperlink and parenthetical citation practices in webtext journals Kairos and Computers and Composition Online, via discourse-based interviews with several authors and editors for each journal. Chapter V draws parallels among my investigations and ultimately concludes with a proposal for a new kind of hytpertextual academic publication aimed at “the public”; it offers, at its close, some documents intended to sketch the shape of such a publication, including a “Rhetoric of Hypermedia” style guide for authors