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.

Let me give you an example: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.
Question: Are results reported only for English data?
The answer to this example can be: No
Here is why: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

OK. solve this:
To discover topics from the collected tweets, we used a topic modeling approach that fuzzy clusters the semantically related words such as assigning "diabetes", "cancer", and "influenza" into a topic that has an overall "disease" theme BIBREF44 , BIBREF45 . Among topic models, Latent Dirichlet Allocation (LDA) BIBREF49 is the most popular effective model BIBREF50 , BIBREF19 as studies have shown that LDA is an effective computational linguistics model for discovering topics in a corpus BIBREF51 , BIBREF52 . We used the Mallet implementation of LDA BIBREF49 , BIBREF56 with its default settings to explore opinions in the tweets.
Question: How were topics of interest about DDEO identified?
Answer:
using topic modeling model Latent Dirichlet Allocation (LDA)