Antisemitismexists.
Antisemitism is real—not a distant issue. We gather daily examples of hateful language from social platforms to expose its persistence and use data-driven insights to drive solutions for change.
Warning: The following tweets contain explicit language and hate speech. Viewer discretion is advised.
Daily Hate Exposed
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How You Can Help
Submit Content
If you see antisemitic posts or comments, please share them with us. Your submissions help document antisemitism and drive meaningful change through data and awareness.
Submit Content for DocumentationYour submissions are processed and added to our database immediately
We analyze submissions using natural language processing to identify hate speech patterns
Other Ways to Help
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Share our findings with your network to raise awareness
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Report antisemitic content on social media platforms
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Check our website regularly for updated research findings and analysis
Antisemitic Hate Crime Statistics
Tracking incidents and trends across platforms
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How to Help Fix This?Data.
Through systematic data collection and analysis, we transform individual incidents into actionable insights, enabling targeted responses to combat antisemitism effectively.
Natural Language Analysis
Advanced AI models analyzing antisemitic content patterns
Topic Modeling (LDA K=5)
Top Tokens
Metrics
- • similarity: 0.88
- • examples: 0: The Holocaust never happened, 1: Six million is a lie
Top Tokens
Metrics
- • imperativeVerbs: 61.2%
- • treeDepth: 4.2
Top Tokens
Metrics
- • entities: George Soros: 73%, Rothschild: 64%
Top Tokens
Metrics
- • sarcasm: 22.4%
- • sentenceLength: 13.7
Top Tokens
Metrics
- • naziLanguage: +3.6x
- • emotionalIntensity: 0.89
Analysis of Antisemitic Language Patterns
Analyzing patterns in antisemitic incidents where "Jew" appears as a standalone term
Analysis Focus
Examining incidents where "Jew" appears as a standalone term to identify patterns in antisemitic rhetoric
Data Points
Each point represents a specific incident, with position and characteristics revealing language patterns
Time Evolution
Tracking how antisemitic language and rhetoric have evolved over time in digital spaces
Key Insights
- •Language Categories
Incidents are categorized into threats, slurs, and profanity to identify patterns in antisemitic rhetoric.
- •Text Analysis
Analysis of quoted text reveals patterns in language use, intensity, and style across different incidents.
- •Temporal Patterns
Tracking how incidents and their characteristics change over time to understand evolving patterns.
Methodology
- •Text Extraction
Identifying and extracting quoted text containing the word "Jew" as a standalone term.
- •Pattern Recognition
Analyzing text for patterns in language use, including threats, slurs, and profanity.
- •Data Visualization
Creating interactive 3D visualizations to explore relationships between different aspects of the incidents.
Statistical Research
Coming April 2025
Leveraging advanced AI and data analytics to uncover deeper insights into antisemitic patterns and trends. Publishing April 2025.