Data from: Limits of use of social media for monitoring biosecurity events

Compared to applications that trigger massive information streams, like earthquakes and human disease epidemics, the data input for agricultural and environmental biosecurity events (ie. the introduction of unwanted exotic pests and pathogens), is expected to be sparse and less frequent. To investigate if Twitter data can be useful for the detection and monitoring of biosecurity events, we adopted a three-step process. First, we confirmed that sightings of two migratory species, the Bogong moth (Agrotis infusa) and the Common Koel (Eudynamys scolopaceus) are reported on Twitter. Second, we developed search queries to extract the relevant tweets for these species. The queries were based on either the taxonomic name, common name or keywords that are frequently used to describe the species (symptomatic or syndromic). Third, we validated the results using ground truth data. Our results indicate that the common name queries provided a reasonable number of tweets that were related to the ground truth data. The taxonomic query resulted in too small datasets, while the symptomatic queries resulted in large datasets, but with highly variable signal-to-noise ratios. No clear relationship was observed between the tweets from the symptomatic queries and the ground truth data. Comparing the results for the two species showed that the level of familiarity with the species plays a major role. The more familiar the species, the more stable and reliable the Twitter data. This clearly presents a problem for using social media to detect the arrival of an exotic organism of biosecurity concern for which public is unfamiliar.相较于地震、人类传染病疫情等可引发海量信息流的应用场景，农业与环境生物安全（agricultural and environmental biosecurity）事件（即有害外来生物与病原体的传入）相关的数据输入通常较为稀疏且发生频率更低。为探究推特(Twitter)数据能否用于生物安全事件的监测与预警，本研究采用了三步研究流程：首先，我们确认推特平台上存在两种迁徙物种的目击报告，分别为波戈夜蛾（*Agrotis infusa*，英文俗名Bogong moth）与噪鹃（*Eudynamys scolopaceus*，英文俗名Common Koel）；其次，我们构建了针对这两个物种的搜索查询词以提取相关推文，查询词的构建依据涵盖物种的分类学名称、通用俗名，或是用于描述该物种的高频关键词（包括症状相关或综合征相关表述）；最后，我们以实地实测数据（ground truth data）对研究结果进行验证。研究结果显示，基于通用俗名的查询词可返回与实地实测数据关联度较高的有效推文；基于分类学名称的查询词返回的数据集规模过小，而基于症状相关表述的查询词虽能返回大规模数据集，但信噪比波动极大。基于症状相关查询词返回的推文与实地实测数据之间未呈现显著关联。对两个物种的结果对比分析表明，公众对物种的熟悉程度是关键影响因素：物种的公众熟知度越高，推特数据的稳定性与可靠性就越强。这一结论为利用社交媒体监测公众陌生的外来生物安全风险物种的传入带来了明显挑战。