by Yin Yin Lu (University of Oxford)
From 17-19 May I attended the CLARIN workshop on the ‘Creation and Use of Social Media Resources’ in Kaunas, Lithuania. The thirty participants represented a broad range of backgrounds: computer science, corpus linguistics, political science, sociology, communication and media studies, sociolinguistics, psychology, and journalism. Our goal was to share best practises in the large-scale collection and analysis of social media data, particularly from a natural language processing (NLP) perspective.
As Michael Beißwenger noted during the first workshop session, there is a ‘social media gap’ in the corpus linguistics landscape. This is because social media corpora are the “naughty stepchild” of text and speech corpora. Traditional natural language processing tools (for, e.g., news articles, political documents, speeches, essays, books) are not always appropriate for social media texts, given the unique communicative characteristics of such texts. Part-of-speech tagging, tokenisation, dependency parsing, sentiment analysis, irony detection, and topic modelling are notoriously difficult. In addition, the personal nature of much social media creates legal and ethical challenges for the data mining and dissemination of social media corpora: Twitter, for example, forbids researchers from publishing collections of tweets; only their IDs can be shared.
I made invaluable connections with researchers at the intersection of NLP and social media data – and Twitter data in particular, which is the area of my own research. Dirk Hovy, an associate professor at the University of Copenhagen, spoke broadly about the challenges of NLP: engineers assume that all language is identically and independently distributed. This is clearly not true, as language is driven by demographic differences. How can we add extra-linguistic information to NLP models? His proposed solution is word embedding: transforming words into vectors, trained on large amounts of data from different demographic groups. These vectors should capture the linguistic peculiarities of the groups.
A variant of word embedding is document embedding – and tweets can be treated as documents. Thus, it should be possible to transform tweets into vectors to capture the demographic-driven linguistic differences that they contain. I will be applying this approach to my own corpus of 12 million tweets related to the EU referendum.
Andrea Cimino, a postdoc from the Italian NLP Lab, spoke about his work on adapting existing NLP tools—which are trained on traditional text—for social media text. The NLP Lab has developed the best POS tagger for social media based upon deep neural networks (long short-term memory), which are able to capture long relationships between words in a sentence. The tagger has achieved 93.2% accuracy, and is currently only valid on Italian texts. Similar taggers can be developed for English texts, given the appropriate training data.
Rebekah Tromble, an assistant professor at Leiden University, presented on the limitations and biases of data collected from Twitter’s Application Programming Interface (API). There are two public APIs that can be used: the historic Search API and the real-time Streaming API. Up to 18,000 tweets can be harvested from the former over the last seven to ten-day period, whichever limit is reached first. The Streaming API allows for up to 1% of all tweets to be collected in real time; as there are 500 million tweets a day, this is approximately 5 million tweets a day.