1,721,088 research outputs found
Myths and Challenges in Knowledge Extraction and Big Data Analysis on Human-Generated Content from Web and Social Media Sources
Full text linkWhatever people produce on digital media can be a relevant
source of knowledge and behavioural analysis. This is the subject of
interest of a wide part of the new discipline known as Web Science. However,
special care must be exercised when setting up studies on this kind
of sources. Indeed, these studies rarely satisfy the established scientific
method guidelines, because of the nature and size of the data, as well
as because of the bias and scarce generalizability of results. This paper
identifies some of the most crucial challenges that need to be addressed
when tackling knowledge extraction and data analysis out of observational
studies on human-generated content
Enterprise Crowd Computing for Human Aided Chatbots
No full textchatbot is an example of cognitive computing system that emulates human conversations to provide informational, transactional,and conversational services. Despite their widespread adoption, chatbots still suffer from a number of performance issue due to limitations with their programming and training. In this paper we discuss Human Aided Chatbots, i.e. chatbots that rely on humans in the loop to operate. Human Aided Chatbots exploit human intelligence, brought for instance by crowd workers or full-time employees, to fill the gaps caused by limitations of fully automated solutions. A recent example of Human Aided Chatbots is Facebook M. To achieve broader adoption, Human Aided Chatbots must overcome a number of issues, including scalability, low-latency, and privacy. In this short paper, we discuss how Crowd Computing performed in the enterprise could help overcoming such issues. We present some recentfi ndings in thefi eld of Enterprise Crowd Computing, and introduce ECrowd, a platform for enterprise crowd computingdesigned for gathering training data for cognitive systems.Accepted author manuscriptWeb Information System
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Analysis and Knowledge Extraction from Event-related Visual Content on Instagram
Full text linkNowadays people share everything on online social networks,
from daily life stories to the latest local and global news and events. Many
researchers have exploited this as a source for understanding the user
behaviour and profile in various settings. In this paper, we propose two
quantitative methods that investigate the relevance of the published photos
about a cultural event in terms of knowledge that can be extracted,
user behaviour and relation to the context of the event. We show our
approach at work for the monitoring of participation to a large-scale
artistic installation that collected more than 1.5 million visitors in just
two weeks (namely The Floating Piers, by Christo and Jeanne-Claude).
We report our findings and discuss the pros and cons of the analysis
Variations on the Author
Full text link“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
Full text linkWe 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
Design criteria to model groups in big data scenarios: algorithms and best practices
Full text linkThere are different types of information systems, such as those that perform group recommendations and market segmentations, which operate with groups of users. In order to combine the individual preferences and properly address suggestions to users, group modeling strategies are employed. Nowadays, data is characterized by large amounts in terms of volume, speed, and variety (the so-called big data issue). In this paper, we are going to tackle the problem of modeling group preferences in big data scenarios. This study will present the existing strategies, and we are going to present criteria to design the algorithms that implement them when big amounts of data have to be combined. Moreover, a set of best practices discusses under which conditions the presented strategies can be adopted in big data scenarios
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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