instruction:
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.
question:
 We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). 
 Question: What text-based features are used?
answer:
language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities)  language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words)


question:
Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer – a difference of a few seconds would not matter much to the user. Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics.  Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos.  
 Question: What evaluation metrics were used in the experiment?
answer:
For sentence-level prediction they used tolerance accuracy, for segment retrieval accuracy and MRR and for the pipeline approach they used overall accuracy


question:
We aim to find such content in the social media focusing on the tweets. 
 Question: Does the dataset contain content from various social media platforms?
answer:

No