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Demographic Biases of Crowd Workers in Key Opinion Leaders Finding

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Hossein A. Rahmani, Jie Yang

Information Retrieval · Submitted 18 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.

how it is stated · asserted 0.98 read the record →

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arXiv ID2110.09248 (version 2)
Pith IDA5VCYTVD, Pith's permanent record of this paper and its review
CitationHossein A. Rahmani, Jie Yang. "Demographic Biases of Crowd Workers in Key Opinion Leaders Finding." arXiv:2110.09248 (2021). https://pith.science/paper/A5VCYTVD
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,800 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.14 · human participants 0.74 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.07 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. background 1.00 0.03 0.02 asserted 1.00
s1 Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. background 0.99 0.06 0.03 asserted 1.00
s2 However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. background 0.90 0.06 0.05 asserted 1.00
s3 To avoid such demographic biases, we need to measure how biased each crowd worker is. background 0.48 0.08 0.05 asserted 1.00
s4 In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. method 0.58 0.89 0.21 asserted 1.00
s5 We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset. interpretation 0.75 0.42 0.30 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.88.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.

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Found among the papers closest to this one by title and main claim. Pith's classifier compared each pair's main results and a second model family confirmed the relation; checked against two further families, shown conflicts agreed 118 times in 120 and shown same results 218 times in 239. The quoted sentence is the other paper's main claim.

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