In the attention economy, sensational content exposes consumers to excessive emotional stimulation, hindering calm decision-making. This study proposes Multi-Agent LLM-based Emotional deToxification (MALLET), a multi-agent information sanitization system consisting of four agents: Emotion Analysis, Emotion Adjustment, Balance Monitoring, and Personal Guide. The Emotion Analysis Agent quantifies stimulus intensity using a 6-emotion BERT classifier, and the Emotion Adjustment Agent rewrites texts into two presentation modes, BALANCED (neutralized text) and COOL (neutralized text + supplementary text), using an LLM. The Balance Monitoring Agent aggregates weekly information consumption patterns and generates personalized advice, while the Personal Guide Agent recommends a presentation mode according to consumer sensitivity. Experiments on 800 AG News articles demonstrated significant stimulus score reduction (up to 19.3%) and improved emotion balance while maintaining semantic preservation. Near-zero correlation between stimulus reduction and semantic preservation confirmed that the two are independently controllable. Category-level analysis revealed substantial reduction (17.8-33.8%) in Sports, Business, and Sci/Tech, whereas the effect was limited in the World category, where facts themselves are inherently high-stimulus. The proposed system provides a framework for supporting calm information reception of consumers without restricting access to the original text.
Figures & tables
Fig. 1: Overview of the MALLET System.
Mode
Sˉ
HIR
EBI
FRE
RAW (original)
0.482
0.446
0.316
39.91
BALANCED (neutralized)
0.419
0.354
0.338
30.23
COOL (neutralized + suppl.)
0.389
0.330
0.346
34.54
TABLE I: Primary Metrics across Presentation Modes.
Metric
Comparison
t
padj
d
S
RAW vs BAL
5.93
1.3×10−8
0.210
S
RAW vs COOL
7.97
2.1×10−14
0.282
EBI
RAW vs BAL
− 4.26
2.3×10−5
− 0.151
EBI
RAW vs COOL
− 5.67
3.9×10−8
− 0.201
FRE
RAW vs BAL
17.23
5.2×10−56
0.609
FRE
RAW vs COOL
9.60
4.9×10−20
0.339
TABLE II: Results of Paired t -Tests (Holm Correction, n=800 ).
Category
SˉRAW
SˉBAL
SˉCOOL
Δ BAL
Δ COOL
World
0.659
0.660
0.645
− 0.1%
2.2%
Sports
0.393
0.312
0.260
20.6%
33.8%
Business
0.500
0.396
0.350
20.8%
30.0%
Sci/Tech
0.374
0.308
0.299
17.8%
20.0%
TABLE III: Mean Stimulus Score by Category.
Fig. 2: Mean Probability Distribution of the Six Emotions across the Three Conditions.
Verification method
BALANCED
COOL
SBERT similarity (mean)
0.846
0.827
NLI entailment rate
95.1%
27.8%
NLI neutral rate
3.0%
68.6%
NLI contradiction rate
1.9%
3.6%
TABLE IV: Results of Semantic Preservation Verification.
Fig. 3: Scatter Plot of Stimulus Reduction Magnitude and SBERT Similarity.
Consumer ID
Sˉ
HIR
EBI
anger
0
0.540
0.53
0.615
0.398
1
0.503
0.46
0.641
0.322
2
0.537
0.50
0.609
0.364
3
0.459
0.44
0.616
0.313
4
0.457
0.41
0.603
0.328
TABLE V: Weekly Summary of Pseudo-Consumers.
ID
Advice
0
Your dominant emotion this week is anger at 39.8%, combined with a high HIR of 0.53, indicating frequent engagement with conflict-driven news. Try diversifying your news consumption toward science, technology, or positive stories. Additionally, switching your display mode to COOL or BALANCED can help moderate emotional impact.
3
Your dominant emotion this week is joy (0.404), with moderate anger at 0.313 and HIR of 0.44. To maintain a positive balance while reducing anger, try incorporating more science and technology stories. Since your HIR is below 0.5, you can continue with your current display mode but consider mixing in more balanced topics.
TABLE VI: Examples of Personalized Advice.
ID
Rec.
Rationale
P1
COOL
Gentle and context-rich rewriting reduces anxiety and prevents being overwhelmed.
P2
BAL
Compatibility between factual accuracy and moderate emotional tone suits a balanced consumer.
P3
RAW
Preserves the boldness and dramatic expression of the original while maintaining accuracy, matching the stimulus-seeking preference.
TABLE VII: Mode Recommendation Results Based on Consumer Personas.