Question: How many annotators tagged each text?

Evidence:
raw_evidence: The final code-mixed tweets were forwarded to a group of three annotators who were university students and fluent in both English and Hindi. Approximately 60 hours were spent in tagging tweets for the presence of humor. Tweets which consisted of any anecdotes, fantasy, irony, jokes, insults were annotated as humorous whereas tweets stating any facts, dialogues or speech which did not contain amusement were put in non-humorous class. Following are some examples of code-mixed tweets in the corpus:
raw_evidence: The final code-mixed tweets were forwarded to a group of three annotators who were university students and fluent in both English and Hindi. Approximately 60 hours were spent in tagging tweets for the presence of humor. Tweets which consisted of any anecdotes, fantasy, irony, jokes, insults were annotated as humorous whereas tweets stating any facts, dialogues or speech which did not contain amusement were put in non-humorous class. Following are some examples of code-mixed tweets in the corpus:
Annotators were given certain guidelines to decide whether a tweet was humorous or not. The context of the tweet could be found by searching about hashtag or keywords used in the tweet. Example (1) uses a hashtag `#WontGiveItBack' which was trending during the ICC cricket world cup 2015. Searching it on Google gave 435k results and the time of the tweet was after the final match of the tournament. So there is an observational humor in (1) as India won the world cup in 2011 and lost in 2015 , hence the tweet was classified as humorous. Any tweets stating any facts, news or reality were classified as non-humorous. There were many tweets which did not contain any hashtags, to understand the context of such tweets annotators selected some keywords from the tweet and searched them online. Example (2) contains a comment towards a political leader towards development and was categorized as non-humorous. Tweets containing normal jokes and funny quotes like in (3) and (4) were put in humorous category. There were some tweets like (5) which consists of poem or lines of a song but modified. Annotators were guided that if such tweets contains satire or any humoristic features, then it could be categorized as humorous otherwise not. There were some tweets which were typical to categorize like (5), hence it was left to the annotators to the best of their understanding. Based on the above guidelines annotators categorized the tweets. To measure inter annotator agreement we opted for Fleiss' Kappa BIBREF12 obtaining an agreement of 0.821 . Both humorous and non-humorous tweets in nearly balanced amount were selected to prepare the corpus. If we had included humorous tweets from one domain like sports and non humorous tweets from another domain like news then, it would have given high performance of classification BIBREF13 . To classify based on the semantics and not on the domain differences, we included both types of tweets from different domains. Many tweets contains a picture along with a caption. Sometimes a caption may not contain humor but combined with the picture, it can provide some degree of humor. Such tweets were removed from the corpus to make the corpus unimodal. In Figure 1, the first tweet, “Anurag Kashyap can never join AAP because ministers took oath `main kisi Anurag aur dwesh ke bina kaam karunga' ” (Anurag Kashyap can never join AAP because ministers took oath `I will work without any affection (Anurag in Hindi) and without hesitation (dwesh in Hindi)'), was classified as humorous. The second tweet, “#SakshiMalik take a bow! #proudIndian #Rio #Olympics #BronzeMedal #girlpower Hamaara khaata khul Gaya!” (#SakshiMalik take a bow! #proudIndian #Rio #Olympics #BronzeMedal #girlpower Our account opened!) was classified as non-humorous as it contains a pride statement.

Answers:
three 
three annotators