InfoAffect: A Dataset for Affective Analysis of Infographics

• URL: http://arxiv.org/abs/2511.06404v1
• Date: Sun, 09 Nov 2025 14:35:59 GMT
• Title: InfoAffect: A Dataset for Affective Analysis of Infographics
• Authors: Zihang Fu, Yunchao Wang, Chenyu Huang, Guodao Sun, Ronghua Liang,
• Abstract summary: We introduce a 3.5k-sample affect-annotated InfoAffect dataset, which combines textual content with real-world infographics.<n>Five state-of-the-art multimodal large language models (MLLMs) then analyze both modalities, and their outputs are fused with Reciprocal Rank Fusion (RRF) algorithm to yield robust affects and confidences.
• Score: 21.63643063062395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Infographics are widely used to convey complex information, yet their affective dimensions remain underexplored due to the scarcity of data resources. We introduce a 3.5k-sample affect-annotated InfoAffect dataset, which combines textual content with real-world infographics. We first collect the raw data from six domains and aligned them via preprocessing, the accompanied-text-priority method, and three strategies to guarantee the quality and compliance. After that we construct an affect table and use it to constrain annotation. Five state-of-the-art multimodal large language models (MLLMs) then analyze both modalities, and their outputs are fused with Reciprocal Rank Fusion (RRF) algorithm to yield robust affects and confidences. We conducted a user study with two experiments to validate usability and assess InfoAffect dataset using the Composite Affect Consistency Index (CACI), achieving an overall score of 0.986, which indicates high accuracy.

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