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    <titleInfo>
        <title>Objectifying the Subjective: Cognitive Biases in Topic Interpretations</title>
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    <name type="personal">
        <namePart type="given">Swapnil</namePart>
        <namePart type="family">Hingmire</namePart>
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        <namePart type="given">Ze</namePart>
        <namePart type="given">Shi</namePart>
        <namePart type="family">Li</namePart>
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        <namePart type="given">Shiyu</namePart>
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        <namePart type="family">Zeng</namePart>
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    <name type="personal">
        <namePart type="given">Ahmed</namePart>
        <namePart type="given">Musa</namePart>
        <namePart type="family">Awon</namePart>
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    <name type="personal">
        <namePart type="given">Luiz</namePart>
        <namePart type="given">Franciscatto</namePart>
        <namePart type="family">Guerra</namePart>
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    <abstract>Interpretation of topics is crucial for their downstream applications. State-of-the-art evaluation measures of topic quality such as coherence and word intrusion do not measure how much a topic facilitates the exploration of a corpus. To design evaluation measures grounded on a task, and a population of users, we do user studies to understand how users interpret topics. We propose constructs of topic quality and ask users to assess them in the context of a topic and provide rationale behind evaluations. We use reflexive thematic analysis to identify themes of topic interpretations from rationales. Users interpret topics based on availability and representativeness heuristics rather than probability. We propose a theory of topic interpretation based on the anchoring-and-adjustment heuristic: users anchor on salient words and make semantic adjustments to arrive at an interpretation. Topic interpretation can be viewed as making a judgment under uncertainty by an ecologically rational user, and hence cognitive biases aware user models and evaluation frameworks are needed.</abstract>
    <identifier type="citekey">hingmire-etal-2025-objectifying</identifier>
    <identifier type="doi">10.1162/tacl.a.50</identifier>
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    <part>
        <date>2025</date>
        <detail type="volume"><number>13</number></detail>
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            <start>1527</start>
            <end>1559</end>
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%0 Journal Article
%T Objectifying the Subjective: Cognitive Biases in Topic Interpretations
%A Hingmire, Swapnil
%A Li, Ze Shi
%A Zeng, Shiyu (Vivienne)
%A Awon, Ahmed Musa
%A Guerra, Luiz Franciscatto
%A Ernst, Neil
%J Transactions of the Association for Computational Linguistics
%D 2025
%V 13
%I MIT Press
%C Cambridge, MA
%F hingmire-etal-2025-objectifying
%X Interpretation of topics is crucial for their downstream applications. State-of-the-art evaluation measures of topic quality such as coherence and word intrusion do not measure how much a topic facilitates the exploration of a corpus. To design evaluation measures grounded on a task, and a population of users, we do user studies to understand how users interpret topics. We propose constructs of topic quality and ask users to assess them in the context of a topic and provide rationale behind evaluations. We use reflexive thematic analysis to identify themes of topic interpretations from rationales. Users interpret topics based on availability and representativeness heuristics rather than probability. We propose a theory of topic interpretation based on the anchoring-and-adjustment heuristic: users anchor on salient words and make semantic adjustments to arrive at an interpretation. Topic interpretation can be viewed as making a judgment under uncertainty by an ecologically rational user, and hence cognitive biases aware user models and evaluation frameworks are needed.
%R 10.1162/tacl.a.50
%U https://aclanthology.org/2025.tacl-1.70/
%U https://doi.org/10.1162/tacl.a.50
%P 1527-1559

Markdown (Informal)

[Objectifying the Subjective: Cognitive Biases in Topic Interpretations](https://aclanthology.org/2025.tacl-1.70/) (Hingmire et al., TACL 2025)

• Objectifying the Subjective: Cognitive Biases in Topic Interpretations (Hingmire et al., TACL 2025)

ACL

• Swapnil Hingmire, Ze Shi Li, Shiyu (Vivienne) Zeng, Ahmed Musa Awon, Luiz Franciscatto Guerra, and Neil Ernst. 2025. Objectifying the Subjective: Cognitive Biases in Topic Interpretations. Transactions of the Association for Computational Linguistics, 13:1527–1559.