In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
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Question: In this paper, we use three data sets from the literature to train and evaluate our own classifier. Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 .

Answer: Which publicly available datasets are used?


Question: The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset.

Answer: How many users do they look at?


Question: Hence, the contributions of this work are two-fold: (1) we develop a linguistically-infused neural network model to classify reactions in social media posts, and (2) we apply our model to label 10.8M Twitter posts and 6.2M Reddit comments in order to evaluate the speed and type of user reactions to various news sources. We develop a neural network architecture that relies on content and other linguistic signals extracted from reactions and parent posts, and takes advantage of a “late fusion” approach previously used effectively in vision tasks BIBREF13 , BIBREF14 . More specifically, we combine a text sequence sub-network with a vector representation sub-network as shown in Figure FIGREF5 . The text sequence sub-network consists of an embedding layer initialized with 200-dimensional GloVe embeddings BIBREF15 followed by two 1-dimensional convolution layers, then a max-pooling layer followed by a dense layer. The vector representation sub-network consists of two dense layers. We incorporate information from both sub-networks through concatenated padded text sequences and vector representations of normalized Linguistic Inquiry and Word Count (LIWC) features BIBREF16 for the text of each post and its parent.

Answer:
What is the architecture of their model?