1,720,965 research outputs found
Identifying Misaligned Inter-Group Links and Communities
No full textMany social media systems explicitly connect individuals (e.g., Facebook or Twitter); as a result, they are the targets of most research on social networks. However, many systems do not emphasize or support explicit linking between people (e.g., Wikipedia or Reddit), and even fewer explicitly link communities. Instead, network analysis is performed through inference on implicit connections, such as co-authorship or text similarity. Depending on how inference is done and what data drove it, different networks may emerge. While correlated structures often indicate stability, in this work we demonstrate that differences, or misalignment, between inferred networks also capture interesting behavioral patterns. For example, high-text but low-author similarity often reveals communities "at war" with each other over an issue or high-author but low-text similarity can suggest community fragmentation. Because we are able to model edge direction, we also find that asymmetry in degree (in-versus-out) co-occurs with marginalized identities (subreddits related to women, people of color, LGBTQ, etc.). In this work, we provide algorithms that can identify misaligned links, network structures and communities. We then apply these techniques to Reddit to demonstrate how these algorithms can be used to decipher inter-group dynamics in social media.</jats:p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Scientific concept hierarchy dataset extracted from ACMDL.
No full textThere is one file for every “tree” (note that some nodes will repeat between files). In each file there is 1 line per “edge.” The format is: child_concept, parent_concept, child_first_year, parent_first_year. The values are tab-separated. Spaces within concept names are replaced with an underscore. The first year corresponds to first year the concept was detected in the ACMDL corpus (note that this is generally correct, but can be off by a year or two). (ZIP)</p
C <scp>ommunity</scp> D <scp>iff</scp>
No full text
Community detection is an oft-used analytical function of network analysis but can be a black art to apply in practice. Grouping of related nodes is important for identifying patterns in network datasets but also notoriously sensitive to input data and algorithm selection. This is further complicated by the fact that, depending on domain and use case, the ground truth knowledge of the end-user can vary from none to complete. In this work, we present C
ommunity
D
iff
, an interactive visualization system that combines visualization and active learning (AL) to support the end-user’s analytical process. As the end-user interacts with the system, a continuous refinement process updates both the community labels and visualizations. C
ommunity
D
iff
features a mechanism for visualizing
ensemble spaces
, weighted combinations of algorithm output, that can identify patterns, commonalities, and differences among multiple community detection algorithms. Among other features, C
ommunity
D
iff
introduces an AL mechanism that visually indicates uncertainty about community labels to focus end-user attention and supporting end-user control that ranges from explicitly indicating the number of expected communities to merging and splitting communities. Based on this end-user input, C
ommunity
D
iff
dynamically recalculates communities. We demonstrate the viability of our through a study of speed of end-user convergence on satisfactory community labels. As part of building C
ommunity
D
iff
, we describe a design process that can be adapted to other Interactive Machine Learning applications.
</jats:p
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
