Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling

Yi Han; Wen Ding; Xuanjing Chen; Zhiguo Tao; Lifan Sun; Zichen Yuan; Yifei Huang

Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling

Yi Han, Wen Ding, Xuanjing Chen, Zhiguo Tao, Lifan Sun, Zichen Yuan, Yifei Huang

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.

Anthology ID:: 2026.woah-1.1
Volume:: Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Agostina Calabrese, Thomas Davidson, Christine de Kock, Urja Khurana, Marta Marchiori Manerba, Paloma Piot, Zeerak Talat
Venues:: WOAH | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1–12
Language:
URL:: https://aclanthology.org/2026.woah-1.1/
DOI:
Bibkey:
Cite (ACL):: Yi Han, Wen Ding, Xuanjing Chen, Zhiguo Tao, Lifan Sun, Zichen Yuan, and Yifei Huang. 2026. Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling. In Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH), pages 1–12, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling (Han et al., WOAH 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.woah-1.1.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{han-etal-2026-adaptive,
    title = "Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling",
    author = "Han, Yi  and
      Ding, Wen  and
      Chen, Xuanjing  and
      Tao, Zhiguo  and
      Sun, Lifan  and
      Yuan, Zichen  and
      Huang, Yifei",
    editor = "Calabrese, Agostina  and
      Davidson, Thomas  and
      de Kock, Christine  and
      Khurana, Urja  and
      Marchiori Manerba, Marta  and
      Piot, Paloma  and
      Talat, Zeerak",
    booktitle = "Proceedings of the 10th Workshop on Online Abuse and Harms ({WOAH})",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.woah-1.1/",
    pages = "1--12",
    ISBN = "979-8-89176-487-3",
    abstract = "ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="han-etal-2026-adaptive">
    <titleInfo>
        <title>Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yi</namePart>
        <namePart type="family">Han</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wen</namePart>
        <namePart type="family">Ding</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xuanjing</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhiguo</namePart>
        <namePart type="family">Tao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lifan</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zichen</namePart>
        <namePart type="family">Yuan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yifei</namePart>
        <namePart type="family">Huang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Agostina</namePart>
            <namePart type="family">Calabrese</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thomas</namePart>
            <namePart type="family">Davidson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christine</namePart>
            <namePart type="family">de Kock</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Urja</namePart>
            <namePart type="family">Khurana</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="family">Marchiori Manerba</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paloma</namePart>
            <namePart type="family">Piot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Talat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-487-3</identifier>
    </relatedItem>
    <abstract>ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.</abstract>
    <identifier type="citekey">han-etal-2026-adaptive</identifier>
    <location>
        <url>https://aclanthology.org/2026.woah-1.1/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>1</start>
            <end>12</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling
%A Han, Yi
%A Ding, Wen
%A Chen, Xuanjing
%A Tao, Zhiguo
%A Sun, Lifan
%A Yuan, Zichen
%A Huang, Yifei
%Y Calabrese, Agostina
%Y Davidson, Thomas
%Y de Kock, Christine
%Y Khurana, Urja
%Y Marchiori Manerba, Marta
%Y Piot, Paloma
%Y Talat, Zeerak
%S Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-487-3
%F han-etal-2026-adaptive
%X ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.
%U https://aclanthology.org/2026.woah-1.1/
%P 1-12

Download as File

Markdown (Informal)

[Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling](https://aclanthology.org/2026.woah-1.1/) (Han et al., WOAH 2026)

Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling (Han et al., WOAH 2026)

ACL

Yi Han, Wen Ding, Xuanjing Chen, Zhiguo Tao, Lifan Sun, Zichen Yuan, and Yifei Huang. 2026. Adaptive Causal Coordination Detection for Online Harm Campaigns: A Framework Study with Semi-Supervised Labeling. In Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH), pages 1–12, Budapest, Hungary. Association for Computational Linguistics.