Abstract Mining of social media has been shown to be a useful tool for social and biological research (e.g. tracking disease out breaks). This article outlines an accessible approach to the use of text and data mining (TDM) of social media to gather information on wildlife recreation activity. The spatio-temporal distribution of the shore based recreational European seabass (Dicentrarchus labrax) fishery in Wales is used as an example. Public online user generated content was mined using automated scraping. Data on fisher activity and fish sizes were extracted and then georeferenced by matching place names to a custom compiled gazetteer. Numbers of trips and spatio-temporal trends in the distribution of activity and catches were estimated. Prosecution was higher in summer than winter, and gear use and trip durations were consistent during the period 2002–13. Comparisons of TDM with existing surveys showed higher levels of activity and catch, and shorter mean trip durations were estimated using TDM. Monthly activity correlated closely with existing survey data. Spatial and temporal data agreed qualitatively with expert knowledge. This article showed that TDM can be used to describe a wildlife recreation activity, but use of TDM to derive unbiased population level estimates is challenging and more work is required to develop appropriate methods to correct for bias. These methods required no expertise in natural language processing or machine learning, a working knowledge of programming (e.g. in Python or R) is all that is needed to apply this approach. The opportunities to use TDM will increase with the continuing adoption of smartphones in emerging economies and developing nations and is of may be of particular utility where other data is unavailable.

中文翻译：

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社交媒体的文本和数据挖掘以绘制野生动物娱乐活动图

摘要 社交媒体挖掘已被证明是用于社会和生物学研究（例如跟踪疾病爆发）的有用工具。本文概述了一种使用社交媒体的文本和数据挖掘 (TDM) 来收集野生动物娱乐活动信息的无障碍方法。以威尔士岸基休闲欧洲鲈鱼 (Dicentrarchus labrax) 渔业的时空分布为例。使用自动抓取来挖掘公共在线用户生成的内容。提取有关渔民活动和鱼类大小的数据，然后通过将地名与自定义编译的地名词典进行匹配来进行地理参考。对活动和渔获量分布的旅行次数和时空趋势进行了估计。夏季的起诉比冬季高，2002-13 年期间，装备使用和旅行持续时间保持一致。TDM 与现有调查的比较显示更高水平的活动和渔获量，并且使用 TDM 估计了更短的平均旅行持续时间。每月活动与现有调查数据密切相关。时空数据与专家知识定性一致。本文表明 TDM 可用于描述野生动物娱乐活动，但使用 TDM 得出无偏的种群水平估计具有挑战性，需要做更多的工作来开发适当的方法来纠正偏差。这些方法不需要自然语言处理或机器学习方面的专业知识，只需具备编程知识（例如 Python 或 R）即可应用此方法。