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.

Example Input: Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set “who is with who". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.
Example Output: What social media platform is observed?

Example Input: We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira. 
Example Output: where does the data come from?

Example Input: We employ BIBREF73 's weighted lexicon of terms that uses the dataset of 75,394 Facebook users who shared their status, age and gender. Utilizing these two weighted lexicon of terms, we are predicting the demographic information (age or gender) of INLINEFORM0 (denoted by INLINEFORM1 ) using following equation: INLINEFORM2

where INLINEFORM0 is the lexicon weight of the term, and INLINEFORM1 represents the frequency of the term in the user generated INLINEFORM2 , and INLINEFORM3 measures total word count in INLINEFORM4 .
Example Output:
How do this framework facilitate demographic inference from social media?