In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
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Question: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. 
 Question: What is the source of the textual data? 

Answer: Users' tweets


Question: The third person plural pronoun `they' has no gender in English and most other languages (unlike the third person singular pronouns `he', `she', and `it').  If these sentences are translated into French, then `they' in the first sentence should be translated `elles', as referring to Jane and Susan, and `they' in the second sentence should be translated `ils', as referring to Fred and George. A couple of examples: The word `sie' in German serves as both the formal second person prounoun (always capitalized), the third person feminine singular, and the third person plural.  
 Question: What language do they explore?

Answer: English French German 


Question: We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information;  
 Question: What are the baselines?

Answer:
SVM with unigram, bigram, and trigram features SVM with average word embedding SVM with average transformed word embeddings CNN ecurrent Convolutional Neural Networks SVM and deep learning models with comment information