The Great Barrier Reef (GBR) is a heavily visited area, with 2.2 million international and 1.7 million domestic visitors travelling to the region every year. In addition, about one million local residents live on the nearby coast. Both visitors and residents, to some extent, use social media to share content about their surroundings, perceptions and experiences with the Reef. This data can be used for research purposes. This present research explores whether informal information from social media can complement existing citizen science approaches and biophysical monitoring. This report provides findings from an analysis of Twitter posts and public Facebook page posts and comments that are relevant to the GBR. These social media data were analysed in terms of overall volumes, frequency of particular keywords, and sentiment. Relevant messages from Twitter, and posts and comments from Facebook were collected for a 9 month period, between the 1st of July 2016 and the 17th of March 2017. Using several filtering systems, 13,344 relevant tweets and 6,632 posts/comments were downloaded from Twitter and 13 public Facebook pages, respectively. Some 60.6% of selected tweets had geographic coordinates that allow spatial analysis. Tweets predominantly mentioned the physical environment of ‘beaches’, ‘islands’, and the ‘Reef’, in various permutations of these words. Text content varied, and included factual information, neutral statements, slang, and words expressing various emotional states. In contrast, the Facebook posts focussed more on reef-related activities, with a particular emphasis on experiential elements (e.g. “amazing”). Facebook comments contained largely positive emotional language, compared with posts (by the page owner) that focussed more on the attributes of the GBR. Sentiment analysis was undertaken for data from both social media platforms to track whether perceptions and messages were positive or negative. The analysis showed sentiment varied over time and in relation to particular targets (i.e. defined through keywords). Despite an inherent bias towards positive text, both the Twitter and Facebook data displayed interesting variations. Some of these hint at underlying problems, but others appeared to be an artefact of the existing sentiment algorithm. Overall, Facebook posts and comments were much more positive that tweets, highlighting that the analysis of multiple platforms is useful as they fulfil different purposes and roles. Text that mentioned environmental keywords (e.g. dead, bleaching, damaged) was likely to be negative. Surprisingly, however, the relative frequency of such text was low. Discussions of environmental problems were more detailed and richer on Facebook where there is no restriction in length of text. Facebook therefore provides an opportunity to further engage people to learn and protect the GBR; something that is largely unexploited at present. For Twitter, a dedicated hashtag system would allow relevant and useful information to be collected and at the same time empower people to contribute through citizen science. Several recommendations on the use of social media are made at the end of the report, including their active use as a tool to share environmental values and encourage stewardship, the need to monitor and respond, and the opportunity to use information in conjunction with other sources to enhance monitoring. The latter task will be addressed within the larger research project, where social media data will be integrated with other environmental data.