Automated Controversy Detection in Advertisements
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Solution Overview
Problem
Current methods for detecting controversial advertisements are inefficient, relying on test audiences and lacking clear criteria, which can lead to unintended negative public reactions and potential boycotts, especially in the context of online viral content where user-generated data is uncontrolled.
Innovation Solution
A computer-implemented method for automatically detecting controversy in advertisements by extracting semantic and linguistic features from user comments to construct a classifier, determining the percentage of controversial comments, and generating a controversy score to assess the likelihood of a publication causing controversy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test audiences are used to detect controversial advertisements, then some feedback can be obtained, but the detection is inefficient and lacks clear criteria leading to unintended negative reactions
Solution Approach 1:
The patent replaces the mechanical/manual test audience method with an automated computer-based system that uses natural language processing, machine learning classifiers, and algorithmic analysis of user comments and social media data to detect controversy automatically, thereby improving both efficiency and reliability
Solution Approach 2:
The patent introduces an intermediary automated controversy detection system that acts as a mediator between advertisement publication and public reaction, analyzing user feedback through structured criteria and providing early warning before negative perceptions spread virally
2Measurement precision
If clear criteria for controversy detection are established, then detection accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the controversy detection task into distinct components: data collection from multiple sources, preprocessing and cleaning, feature extraction, classification using trained models, and scoring. Each component has clear, defined criteria and operations, making the overall complex system manageable and interpretable
Solution Approach 2:
The patent transforms qualitative controversy assessment into quantitative measurement by defining specific parameters such as controversy scores, sentiment polarity, emotional intensity metrics, and confidence thresholds, enabling precise measurement while maintaining systematic complexity through parameterized models
3Loss of time
If user-generated data from online platforms is monitored, then real-time controversy detection is possible, but the volume of data to be analyzed increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing and cleaning user comments immediately upon collection, removing duplicates, filtering spam, and standardizing formats before the main analysis phase, thereby reducing the volume of data requiring intensive processing while enabling real-time detection
Solution Approach 2:
The patent extracts only the relevant features and key information from large volumes of user comments using natural language processing techniques, focusing on sentiment indicators, emotional keywords, and controversy-related patterns while discarding redundant information, thus analyzing essential content efficiently
4Productivity
If automated classification of comments is implemented, then detection speed increases, but the need for training data and model development increases complexity
Solution Approach 1:
The patent performs preliminary actions by collecting and labeling training data in advance, training the classification models beforehand with diverse examples of controversial and non-controversial comments, and storing pre-trained models for rapid deployment, thereby enabling fast real-time detection without performing complex training during operational phase
Solution Approach 2:
The patent uses copying by creating multiple classification models trained on different datasets and perspectives, ensembling their predictions to improve accuracy while distributing the computational complexity across multiple simpler models rather than one highly complex model
Data Source
AI summary
In the examples provided herein, a controversy detection system includes a classifier engine to classify each of a plurality of comments about a publication as controversial or non-controversial. The system also includes a controversy detector engine to determine, based on the classification of the plurality of comments, whether the publication is controversial or non-controversial.