<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="popovic-etal-2026-dideco">
    <titleInfo>
        <title>DIDECO: An Annotated Dataset for Intent Detection in Digital Communications</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Senaid</namePart>
        <namePart type="family">Popovic</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Damien</namePart>
        <namePart type="family">Riquet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maxime</namePart>
        <namePart type="family">Meyer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fabien</namePart>
        <namePart type="family">Lauer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yannick</namePart>
        <namePart type="family">Parmentier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper presents DIDECO, the first annotated dataset specifically designed for detecting both explicit and implicit intents in digital communications. We address a critical gap in cybersecurity research by developing a comprehensive taxonomy that distinguishes between explicit communicative goals (what is requested) and implicit persuasion mechanisms (how compliance is engineered). Grounded in Speech Act Theory and persuasion psychology principles, our taxonomy encompasses 20 distinct intent categories across explicit and implicit intents. We annotated 220 LLM-generated spear-phishing emails using a multi-label protocol with six trained annotators, yielding 2,162 intent annotations that reveal the layered complexity of malicious communications. Our analysis demonstrates that sophisticated attacks employ multiple concurrent intents, combining explicit communicative goals with implicit persuasion strategies. This dataset provides resources for developing intent-aware detection systems capable of identifying sophisticated social engineering attacks through semantic analysis.</abstract>
    <identifier type="citekey">popovic-etal-2026-dideco</identifier>
    <identifier type="doi">10.63317/3xsu5rdnw8hi</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.542/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>6808</start>
            <end>6822</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T DIDECO: An Annotated Dataset for Intent Detection in Digital Communications
%A Popovic, Senaid
%A Riquet, Damien
%A Meyer, Maxime
%A Lauer, Fabien
%A Parmentier, Yannick
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F popovic-etal-2026-dideco
%X This paper presents DIDECO, the first annotated dataset specifically designed for detecting both explicit and implicit intents in digital communications. We address a critical gap in cybersecurity research by developing a comprehensive taxonomy that distinguishes between explicit communicative goals (what is requested) and implicit persuasion mechanisms (how compliance is engineered). Grounded in Speech Act Theory and persuasion psychology principles, our taxonomy encompasses 20 distinct intent categories across explicit and implicit intents. We annotated 220 LLM-generated spear-phishing emails using a multi-label protocol with six trained annotators, yielding 2,162 intent annotations that reveal the layered complexity of malicious communications. Our analysis demonstrates that sophisticated attacks employ multiple concurrent intents, combining explicit communicative goals with implicit persuasion strategies. This dataset provides resources for developing intent-aware detection systems capable of identifying sophisticated social engineering attacks through semantic analysis.
%R 10.63317/3xsu5rdnw8hi
%U https://aclanthology.org/2026.lrec-1.542/
%U https://doi.org/10.63317/3xsu5rdnw8hi
%P 6808-6822

Markdown (Informal)

[DIDECO: An Annotated Dataset for Intent Detection in Digital Communications](https://aclanthology.org/2026.lrec-1.542/) (Popovic et al., LREC 2026)

• DIDECO: An Annotated Dataset for Intent Detection in Digital Communications (Popovic et al., LREC 2026)

ACL

• Senaid Popovic, Damien Riquet, Maxime Meyer, Fabien Lauer, and Yannick Parmentier. 2026. DIDECO: An Annotated Dataset for Intent Detection in Digital Communications. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6808–6822, Palma de Mallorca, Spain. ELRA Language Resource Association.