Are link-based and citation-based journal metrics correlated? An Open Access megapublisher case study

[EN] The current value of link counts as supplementary measures of the formal quality and impact of journals is analyzed, considering an open access megapublisher (MDPI) as a case study. We analyzed 352 journals through 21 citation-based and link-based journal-level indicators, using Scopus (523,935 publications) and Majestic (567,900 links) as data sources. Given the statistically significant strong positive Spearman correlations achieved, it is concluded that link-based indicators mainly reflect the quality (indexed in Scopus), size (publication output), and impact (citations received) of MDPI's journals. In addition, link data are significantly greater for those MDPI journals covering many subjects (generalist journals). However, nonstatistically significant differences are found between subject categories, which can be partially attributed to the "series title profile" effect of MDPI. Further research is necessary to test whether link-based indicators can be used as informative measures of journals' current research impact beyond the specific characteristics of MDPI.This research has been funded by the Valencian Regional Government (Spain), through the research project UNIVERSEO (Ref. GV/2021/141)Orduña-Malea, E.; Aguillo, IF. (2022). Are link-based and citation-based journal metrics correlated? An Open Access megapublisher case study. Quantitative Science Studies. 3(3):793-814. https://doi.org/10.1162/qss_a_001997938143

Controversy Analysis and Detection

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the filter bubble effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple list of 10 links . This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two themes: computational models of controversy, and controversies occurring in neighborhoods of topics. Our broad contributions are: (1) Presenting a theoretical framework for modeling controversy as contention among populations; (2) Constructing the first automated approach to detecting controversy on the web, using a KNN classifier that maps from the web to similar Wikipedia articles; and (3) Proposing a novel controversy detection in Wikipedia by employing a stacked model using a combination of link structure and similarity. We conclude this work by discussing the challenging technical, societal and ethical implications of this emerging research area and proposing avenues for future work

Users' Traces for Enhancing Arabic Facebook Search

International audienceThis paper proposes an approach on Facebook search in Arabic, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook post. Therefore, the polarity of each comment expressed on a given Facebook post, is estimated on the basis of a neural sentiment model in Arabic language. Secondly, we group signals according to their complementarity using features selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics in Arabic language. Experiments results reveal that Random Forests combined with ReliefFAttributeEval (RLF) was the most effective LTR approach for this task

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

How does website design in the e-banking sector affect customer attitudes and behaviour?

This thesis researches the interface between ebanks and their customers. An industry traditionally based upon personal contact, the rise of ebanking has changed this relationship such that transactions are now mainly conducted via website interfaces. The resultant loss of personal contact between bank and customer has removed many of the cues available to customers upon which judgments of service, reliability and trust were made. The question raised by this change is: what factors influence consumer choice when viewing bank websites? The arguments of this thesis are that user evaluation of websites and their willingness to use those websites is based not only on user centred factors such as motivation, experience and knowledge but also upon their appraisal of website structure and content

Ranking users and information in online social networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (leaf 39).The goal of this project is to explore the design and implementation of SocialRank. SocialRank is a personalized ranking algorithm that provides--for each user--ratings for people in his online social network. Subsequently, these ratings are used to rank incoming information received by the user from those in his social network. We analyze the use of actions on online social networks as proxies for measuring the strength of relationships between users and introduce an action scoring mechanism that uses different factors to evaluate an action's significance. We implement SocialRank in a generic online social network that we build as part of this research project and explore the effectiveness and usefulness of SocialRank.by Abdulrahman I. Tarbzouni.M.Eng

Bootstrapping Web Archive Collections From Micro-Collections in Social Media

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. We investigate the problem of generating seed URIs automatically, and explore the state of the art in collection building and seed selection. Attempts toward generating seeds automatically have mostly relied on scraping Web or social media Search Engine Result Pages (SERPs). In this work, we introduce a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share narratives about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts Micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create Micro-collections is an indication of editorial activity and a demonstration of domain expertise. Therefore, we propose a model for generating seeds from Micro-collections. We begin by introducing a simple vocabulary, called post class for describing social media posts across different platforms, and extract seeds from the Micro-collections post class. We further propose Quality Proxies for seeds by extending the idea of collection comparison to evaluation, and present our Micro-collection/Quality Proxy (MCQP) framework for bootstrapping Web archive collections from Micro-collections in social media

Racial Bias in Expert Quality Assessment: A Study of Newspaper Movie Reviews

Newspaper critics' movie reviews are often used by potential movie viewers as signals of expert quality assessment. In this paper, we assess if there is any racial bias in these critics' reviews, and if so, what impact these biases have on viewer demand. To do this, we develop a dataset that tracks ratings from 68 popular movie critics for 566 movies released in the U.S. between 2003 and 2007. The data also include measures of movie production costs, marketing expenditures, type of movie (i.e. genre, MPAA rating, etc.), actor and director quality measures, audience tastes and critics' gender, experience and race. Despite inclusion of all these controls for movie quality and other drivers of critic ratings, we find that ratings for movies with a black lead actor and all white supporting cast are approximately 6% lower than for other racial compositions. These results appear consistent with implicit discrimination. Using estimates of the impact of critics' ratings on movie revenues, we find that lower critic ratings for black lead-white support movies translate into lost revenues of up to 4% or about $2.57 million on average. In sum, prejudice concerning race roles (e.g., the race of the leader versus supporters/followers) can have a direct impact on critic quality assessment, and thereby alter market outcomes.racial bias, quality assessment, expert ratings, movies