Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and when answers include them, they are rarely to be interpreted what the keywords suggest. This work presents a new corpus of 4,442 yes-no question answer pairs from Twitter (Twitter-YN). The corpus includes question-answer instances from different temporal settings. These settings allow investigating if having older tweets helps understanding more contemporary tweets. Common linguistic features of answers meaning yes, no as well as those whose interpretation remains unknown are also discussed. Experimental results show that large language models are far from solving this problem, even after fine-tuning and blending other corpora for the same problem but outside social media (F1: 0.59). In addition to English, this work presents a Hindi corpus of 3,409 yes-no questions and answers from Twitter (Twitter-YN-hi). Cross lingual experiments are conducted using a distant supervision approach. It is observed that performance of multilingual large language models to interpret indirect answers to yes-no questions in Hindi can be improved when Twitter-YN is blended with distantly supervised data.