Scholarlink

<p>Social networks face growing threats from focal toxic structures (FTSs) that distort discourse and amplify toxic content. This study evaluates network intervention effectiveness of three FTS sets using a Russia–Ukraine conflict dataset (324,769 users): R1 FTS Set (79 structures from baseline WFSA), R2 FTS Set (78 structures from optimized WFSA called WFSA-IP), and R3 FTS Set (40 structures from hybrid selection <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R1 \oplus R2\)</EquationSource> </InlineEquation>, comprising best-performing structures from each disjoint set where <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R1 \cap R2 = \emptyset \)</EquationSource> </InlineEquation>). We assessed intervention performance across four metrics using Bonferroni correction (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\alpha = 0.0125\)</EquationSource> </InlineEquation>). The R2 FTS Set demonstrated superior effectiveness relative to R1 across all metrics, with large effect sizes (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(d = 0.75\)</EquationSource> </InlineEquation>–1.49, all <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(p &lt; 0.0125\)</EquationSource> </InlineEquation>). The R3 hybrid FTS Set consistently outperformed R1 but showed mixed performance versus R2 (1/4 metrics). Results establish clear FTSs’ intervention effectiveness hierarchies: optimized algorithm-identified structures (R2) achieve transformative improvements over baseline structures (R1), while hybrid selection approaches (R3) provide value over baseline but approach performance boundaries constrained by the constituent algorithms’ optimization levels. Findings offer actionable insights for large-scale content moderation using FTS-based intervention strategies with algorithmic diversity considerations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Large-scale toxicity intervention in social networks: evaluating integer programming-optimized focal toxic structures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

查看全文
  • Tope Christopher Falade,
  • Nitin Agarwal

摘要

Social networks face growing threats from focal toxic structures (FTSs) that distort discourse and amplify toxic content. This study evaluates network intervention effectiveness of three FTS sets using a Russia–Ukraine conflict dataset (324,769 users): R1 FTS Set (79 structures from baseline WFSA), R2 FTS Set (78 structures from optimized WFSA called WFSA-IP), and R3 FTS Set (40 structures from hybrid selection \(R1 \oplus R2\) , comprising best-performing structures from each disjoint set where \(R1 \cap R2 = \emptyset \) ). We assessed intervention performance across four metrics using Bonferroni correction ( \(\alpha = 0.0125\) ). The R2 FTS Set demonstrated superior effectiveness relative to R1 across all metrics, with large effect sizes ( \(d = 0.75\) –1.49, all \(p < 0.0125\) ). The R3 hybrid FTS Set consistently outperformed R1 but showed mixed performance versus R2 (1/4 metrics). Results establish clear FTSs’ intervention effectiveness hierarchies: optimized algorithm-identified structures (R2) achieve transformative improvements over baseline structures (R1), while hybrid selection approaches (R3) provide value over baseline but approach performance boundaries constrained by the constituent algorithms’ optimization levels. Findings offer actionable insights for large-scale content moderation using FTS-based intervention strategies with algorithmic diversity considerations.