MCQs

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Overall MCQs for Reference

Which AI technique is commonly used for toxic comment detection?
a. Regression analysis
b. Natural Language Processing (NLP)
c. Clustering
d. Image recognition
Answer: b. Natural Language Processing (NLP)peerj+1
What type of content is typically classified as ‘toxic’ in AI-based comment filters?
a. Spam
b. Hate speech, harassment, or abuse
c. Promotional offers
d. News updates
Answer: b. Hate speech, harassment, or abuseijraset+1
Which model combines CNN and LSTM for improved toxic comment detection?
a. Decision Tree
b. Naive Bayes
c. Hybrid CNN-LSTM
d. K-Nearest Neighbors
Answer: c. Hybrid CNN-LSTMpeerj
What is a major challenge with toxic comment detection in different languages?
a. Lack of computational resources
b. Limited annotation for non-English languages
c. Hardware limitations
d. Network speed
Answer: b. Limited annotation for non-English languagespeerj
Which dataset was specifically developed for researching toxic comments?
a. MNIST
b. PARADETOX
c. CIFAR-10
d. ImageNet
Answer: b. PARADETOXpeerj
What is the primary goal of toxic comment detection systems?
a. Monetization of platforms
b. Automated moderation of harmful content
c. Advertising optimization
d. CAPTCHA bypassing
Answer: b. Automated moderation of harmful contentijraset+1
Which feature extraction method is widely used for toxic comment classification?
a. Min-Max Scaling
b. Bag-of-Words
c. Spectral Embedding
d. Decision Tables
Answer: b. Bag-of-Wordspeerj
In toxic comment AI, what does BERT stand for?
a. Binary Extraction Random Tree
b. Bidirectional Encoder Representations from Transformers
c. Bayesian Encoder Random Trees
d. Best Effective Recommendation Tool
Answer: b. Bidirectional Encoder Representations from Transformerspeerj
Which algorithm was found especially effective for detecting toxicity in Roman Urdu?
a. KNN
b. SVM
c. Unigram word model
d. Polynomial regression
Answer: c. Unigram word modelpeerj
Which deep learning method improved accuracy of toxic comment classification by 5% over baselines?
a. Decision Tree Learning
b. Deep Neural Network (DNN)
c. Clustering
d. Linear Regression
Answer: b. Deep Neural Network (DNN)peerj
In Kaggle’s Toxic Comment Challenge, models must detect which type of content?
a. Animal names
b. Obscene, threats, insults, hate speech
c. Sports news
d. Financial data
Answer: b. Obscene, threats, insults, hate speechkaggle
What is a primary weakness of traditional keyword-based toxic comment filters?
a. Too complex
b. Easily bypassed by spelling variations or slang
c. Require GPU hardware
d. Only work on images
Answer: b. Easily bypassed by spelling variations or slangijraset+1
What are the two main classes in toxic comment classification tasks?
a. Spam/Not Spam
b. Toxic/Non-toxic
c. Black/White
d. Male/Female
Answer: b. Toxic/Non-toxicarxiv+1
What is a significant societal risk posed by AI-moderated platforms failing to detect toxicity?
a. Lower meme quality
b. Fueling polarization and societal issues
c. Increased internet speed
d. Reduced ad revenue
Answer: b. Fueling polarization and societal issuesarxiv
Which architecture is well-suited for classifying sequential data such as text?
a. Convolutional Neural Network (CNN)
b. k-Means
c. Support Vector Machine (SVM)
d. Linear Regression
Answer: a. Convolutional Neural Network (CNN)peerj
What is the major limitation in detecting toxicity in cursive languages like Urdu?
a. Limited end-user devices
b. Lack of annotated datasets
c. Network delay
d. Too many models
Answer: b. Lack of annotated datasetspeerj
Google’s Perspective API is used for:
a. Summarizing documents
b. Detecting and scoring comment toxicity
c. Editing videos
d. Generating music
Answer: b. Detecting and scoring comment toxicitypeerj
Which method reduces human labeling bias in toxic comment datasets?
a. Crowdsourcing with balancing
b. Algorithmic rebalancing
c. Outlier removal
d. Pseudorandom sampling
Answer: a. Crowdsourcing with balancingpeerj
What machine learning metric is often used to evaluate toxic comment classifiers?
a. Throughput
b. Accuracy
c. Image resolution
d. Disk usage
Answer: b. Accuracytijer+1
What is a common form of bias in toxic comment detection models?
a. Algorithmic bias through biased training data
b. Battery drain
c. High latency
d. Lack of GPUs
Answer: a. Algorithmic bias through biased training datapeerj
Which linguistic feature type is often used to improve toxic comment classification accuracy?
a. Word count only
b. N-grams
c. Image pixels
d. Audio spectrograms
Answer: b. N-gramspeerj
Why is real-time toxic comment detection important for social platforms?
a. For increasing ad views
b. For enabling immediate moderator response
c. For reducing hardware costs
d. For offline use
Answer: b. For enabling immediate moderator responseijraset
Which approach outperformed multilingual models in toxic comment detection?
a. Conventional rule-based filters
b. BERT
c. Manual review
d. Text-to-speech
Answer: b. BERTpeerj
Which of the following is NOT a toxic comment label in typical AI datasets?
a. Obscene
b. Threat
c. Compliment
d. Insult
Answer: c. Complimentkaggle
Which transformer-based model is commonly used in comment toxicity detection?
a. Random Forest
b. ELECTRA
c. AdaBoost
d. Decision Tree
Answer: b. ELECTRApeerj
Which method can introduce “epistemic uncertainty” in toxic comment detection models?
a. Rule-based tagging
b. Uncertainty estimation frameworks
c. Image captions
d. Shopping data analytics
Answer: b. Uncertainty estimation frameworkspeerj
What is the effect of multi-layering in CNN models for toxic comment detection?
a. No effect
b. Performance improvement
c. Reduced storage
d. Increased label noise
Answer: b. Performance improvementpeerj
Which language’s toxic comments remain a challenge due to cursive text?
a. Mandarin
b. Urdu
c. Spanish
d. German
Answer: b. Urdupeerj
How do dataset labeling inconsistencies affect toxic comment classifiers?
a. Increase generalization
b. Decrease predictive accuracy
c. Improve diversity
d. None
Answer: b. Decrease predictive accuracypeerj
What is the main function of tokenization in toxic comment classification for cursive texts?
a. Handle misspellings
b. Divide text into processable units
c. Remove images
d. Add emojis
Answer: b. Divide text into processable unitspeerj
What challenge is unique to toxic comment datasets for social networks?
a. Uniformity in text quality
b. High volume and variance in slang and spelling
c. Fixed comment length
d. Only numerical data
Answer: b. High volume and variance in slang and spellingtijer+1
What has been a proposed improvement for classifying toxicity in multilingual datasets?
a. Using one-hot encoding only
b. Using advanced transformers like BERT
c. Only punctuation analysis
d. Audio preprocessing
Answer: b. Using advanced transformers like BERTpeerj
Which research highlighted the impact of human judgment bias in crowdsourced toxic comment datasets?
a. Wulczyn, Thain & Dixon (2016)
b. Smith et al. (2009)
c. Jones et al. (2023)
d. Gupta & Singh (2020)
Answer: a. Wulczyn, Thain & Dixon (2016)peerj
Why does toxic comment detection require continuous model updates?
a. Evolving language and tactics
b. Hardware upgrades
c. Changes in IP addresses
d. File format changes
Answer: a. Evolving language and tacticsarxiv+1
Which model is robust in identifying subtle toxic content?
a. Linear Regression
b. BERT
c. K-means clustering
d. Principal Component Analysis
Answer: b. BERTpeerj
What problem does the Perspective API exhibit in toxicity detection tasks?
a. Always underestimates toxicity
b. Susceptible to adversarial manipulation
c. Expensive to run
d. Ignores punctuation
Answer: b. Susceptible to adversarial manipulationpeerj
What is the importance of precision and recall in toxic comment detection?
a. Memory usage monitoring
b. Evaluating the classifier’s ability to minimize false positives and negatives
c. Measuring image size
d. Calculating network speed
Answer: b. Evaluating the classifier’s ability to minimize false positives and negativesarxiv+1
For toxic comments in online platforms, which data labeling method reduces human bias?
a. One expert labeling
b. Multiple rater consensus
c. Random guessing
d. Single language models
Answer: b. Multiple rater consensuspeerj
What is the key benefit of combining CNN and word embeddings for toxic comment detection?
a. Faster compilation times
b. Improved semantic understanding
c. Larger dataset sizes
d. More emojis
Answer: b. Improved semantic understandingpeerj
How can AI help reduce the spread of toxicity online?
a. By encouraging longer posts
b. By real-time detection and moderation
c. By increasing download speeds
d. By archiving data
Answer: b. By real-time detection and moderationweb+1
Why are manually curated offensive word lists limited in modern toxic comment detection?
a. Inflexible to new forms/slang
b. Too sensitive
c. Use too much memory
d. Only for movies
Answer: a. Inflexible to new forms/slangpeerj
What is “aleatoric uncertainty” in toxic content classification?
a. Inherent model randomness due to data noise
b. Model weight precision
c. CPU core counting
d. Dataset balancing
Answer: a. Inherent model randomness due to data noisepeerj
Which is a critical ethical concern with AI-powered toxic comment detection?
a. Speed of processing
b. Fairness and mitigation of bias
c. Data transmission time
d. Cloud cost
Answer: b. Fairness and mitigation of biasarxiv
Which approach achieved 95.6% accuracy in toxic comment detection?
a. TFIDF with ML models
b. Random guessing
c. Manual review
d. Bag-of-numbers
Answer: a. TFIDF with ML modelspeerj
What is the role of crowdsourced toxicity datasets?
a. To increase ad reach
b. To provide diverse real-world examples for model training
c. To reduce email spam
d. To store images
Answer: b. To provide diverse real-world examples for model trainingpeerj
Serving toxic comment AI models on the client side allows:
a. Larger models
b. Real-time, low-latency feedback
c. Less security
d. Slower response
Answer: b. Real-time, low-latency feedbackweb
Which neural network technique can handle long-range dependencies in text?
a. CNN
b. LSTM
c. k-means
d. SVM
Answer: b. LSTMpeerj
Which form of moderation is completely automated by current AI-based systems?
a. Video analysis
b. Text-based toxicity comment filtering
c. Image watermarking
d. Handwriting recognition
Answer: b. Text-based toxicity comment filteringijraset+1
What is the typical first step in building a toxic comment detection system?
a. Data collection and annotation
b. Model deployment
c. Cloud migration
d. Hardware cooling
Answer: a. Data collection and annotationijraset+1
Which evaluation scenario helps judge robustness of a toxic comment classifier?
a. Testing on data from the exact same source as training
b. Cross-domain generalization testing
c. Only accuracy reporting
d. Only recall reporting
Answer: b. Cross-domain generalization testingpeerj

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