1,720,984 research outputs found
Preferences on a Budget: Prioritizing Document Pairs When Crowdsourcing Relevance Judgments
No full textIn Information Retrieval (IR) evaluation, preference judgments are collected by presenting to the assessors a pair of documents and asking them to select which of the two, if any, is the most relevant. This is an alternative to the classic relevance judgment approach, in which human assessors judge the relevance of a single document on a scale; such an alternative allows to make relative rather than absolute judgments of relevance. While preference judgments are easier for human assessors to perform, the number of possible document pairs to be judged is usually so high that it makes it unfeasible to judge them all. Thus, following a similar idea to pooling strategies for single document relevance judgments where the goal is to sample the most useful documents to be judged, in this work we focus on analyzing alternative ways to sample document pairs to judge, in order to maximize the value of a fixed number of preference judgments that can feasibly be collected. Such value is defined as how well we can evaluate IR systems given a budget, that is, a fixed number of human preference judgments that may be collected. By relying on several datasets featuring relevance judgments gathered by means of experts and crowdsourcing, we experimentally compare alternative strategies to select document pairs and show how different strategies lead to different IR evaluation result quality levels. Our results show that, by using the appropriate procedure, it is possible to achieve good IR evaluation results with a limited number of preference judgments, thus confirming the feasibility of using preference judgments to create IR evaluation collections
Self-configuration of scrambling codes for WCDMA small cell networks
No full textThis paper introduces the problem of Primary Scrambling Code (PSC) selection in small cell networks and proposes a novel optimisation technique. Small cells introduce challenges not present in conventional macrocell scrambling code allocation, including the need for dynamic allocation, scalable distributed allocation algorithms, and support for unplanned and organic deployments. To the best of our knowledge this is the first study addressing the issue of distributed scrambling code selection for small cell networks. We propose a decentralized learning algorithm which does not require any collaboration between the neighbouring base-stations and which finds a feasible allocation whenever one exists. The performance of the algorithm is compared against two variations of a greedy algorithm which is the current 3GPP recommendation and is shown to offer significant performance benefits
Crowd worker strategies in relevance judgment tasks
No full textCrowdsourcing is a popular technique to collect large amounts of human-generated labels, such as relevance judgments used to create information retrieval (IR) evaluation collections. Previous research has shown how collecting high quality labels from a crowdsourcing platform can be challenging. Existing quality assurance techniques focus on answer aggregation or on the use of gold questions where ground-truth data allows to check for the quality of the responses. In this paper, we present qualitative and quantitative results, revealing how different crowd workers adopt different work strategies to complete relevance judgment tasks efficiently and their consequent impact on quality. We delve into the techniques and tools that highly experienced crowd workers use to be more efficient in completing crowdsourcing micro-tasks. To this end, we use both qualitative results from worker interviews and surveys, as well as the results of a data-driven study of behavioral log data (i.e., clicks, keystrokes and keyboard shortcuts) collected from crowd workers performing relevance judgment tasks. Our results highlight the presence of frequently used shortcut patterns that can speed-up task completion, thus increasing the hourly wage of efficient workers. We observe how crowd work experiences result in different types of working strategies, productivity levels, quality and diversity of the crowdsourced judgments
The Effects of Crowd Worker Biases in Fact-Checking Tasks
Full text linkDue to the increasing amount of information shared online every day, the need for sound and reliable ways of distinguishing between trustworthy and non-trustworthy information is as present as ever. One technique for performing fact-checking at scale is to employ human intelligence in the form of crowd workers. Although earlier work has suggested that crowd workers can reliably identify misinformation, cognitive biases of crowd workers may reduce the quality of truthfulness judgments in this context. We performed a systematic exploratory analysis of publicly available crowdsourced data to identify a set of potential systematic biases that may occur when crowd workers perform fact-checking tasks. Following this exploratory study, we collected a novel data set of crowdsourced truthfulness judgments to validate our hypotheses. Our findings suggest that workers generally overestimate the truthfulness of statements and that different individual characteristics (i.e., their belief in science) and cognitive biases (i.e., the affect heuristic and overconfidence) can affect their annotations. Interestingly, we find that, depending on the general judgment tendencies of workers, their biases may sometimes lead to more accurate judgments
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
CaptureBias: Supporting Media Scholars with Ambiguity-Aware Bias Representation for News Videos
No full textIn this project we explore the presence of ambiguity in textual and visual media and its influence on accurately understanding andcapturing bias in news. We study this topic in the context of supportingmedia scholars and social scientists in their media analysis. Our focuslies on racial and gender bias as well as framing and the comparisonof their manifestation across modalities, cultures and languages. In thispaper we lay out a human in the loop approach to investigate the role ofambiguity in detection and interpretation of bias.Accepted Author ManuscriptWeb Information System
Characterising and Mitigating Aggregation-Bias in Crowdsourced Toxicity Annotations
No full textTraining machine learning (ML) models for natural language processing usually requires large amount of data, often acquired through crowdsourcing. The way this data is collected and aggregated can have an effect on the outputs of the trained model such as ignoring the labels which differ from the majority. In this paper we investigate how label aggregation can bias the ML results towards certain data samples and propose a methodology to highlight and mitigate this bias. Although our work is applicable to any kind of label aggregation for data subject to multiple interpretations, we focus on the effects of the bias introduced by majority voting on toxicity prediction over sentences. Our preliminary results point out that we can mitigate the majority-bias and get increased prediction accuracy for the minority opinions if we take into account the different labels from annotators when training adapted models, rather than rely on the aggregated labels.Accepted Author ManuscriptWeb Information System
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
Crowd worker strategies in relevance judgment tasks
No full textCrowdsourcing is a popular technique to collect large amounts of human-generated labels, such as relevance judgments used to create information retrieval (IR) evaluation collections. Previous research has shown how collecting high quality labels from a crowdsourcing platform can be challenging. Existing quality assurance techniques focus on answer aggregation or on the use of gold questions where ground-truth data allows to check for the quality of the responses.In this paper, we present qualitative and quantitative results, revealing how different crowd workers adopt different work strategies to complete relevance judgment tasks efficiently and their consequent impact on quality. We delve into the techniques and tools that highly experienced crowd workers use to be more efficient in completing crowdsourcing micro-tasks. To this end, we use both qualitative results from worker interviews and surveys, as well as the results of a data-driven study of behavioral log data (i.e., clicks, keystrokes and keyboard shortcuts) collected from crowd workers performing relevance judgment tasks. Our results highlight the presence of frequently used shortcut patterns that can speed-up task completion, thus increasing the hourly wage of efficient workers. We observe how crowd work experiences result in different types of working strategies, productivity levels, quality and diversity of the crowdsourced judgments
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