一种大数据环境下的在线社交媒体位置推断方法

随着在线社交媒体的快速发展和可定位设备的大量普及,地理位置作为社交媒体大数据中一种质量极高的信息资源,开始在疾病控制、人口流动性分析和广告精准投放等方面得到广泛应用.但是,由于大量用户没有指定或者不能准确指定位置,社交媒体上的地理位置数据十分稀疏.针对此数据稀疏性问题,提出一种基于用户生成内容的位置推断方法UGC-LI(user generate content driven location inference method),实现对社交媒体用户和生成文本位置的推断,为基于位置的个性化信息服务提供数据支撑.通过抽取用户生成文本中的本地词语,构建一个基于词汇地理分布差异和用户社交图谱的概率模型,在多层次的地理范围内推断用户位置.同时,提出一个基于位置的参数化语言模型,计算用户生成文本发出的城市.在真实数据集上进行的评估实验表明:UGC-LI方法能够在15km偏移距离准确定位64.2%的用户,对用户所在城市的推断准确率达到81.3%;同时,可正确定位32.7%的用户生成文本发出的城市,与现有方法相比有明显的提高.

Location Inference Method in Online Social Media with Big Data

As a high-quality source in social media big data, the geographic location has been widely adopted in the fields of disease control, population mobility analysis and ad delivery positioning with the rapid development of online social media and the prevalence of localizable mobile devices. However, the location data are quite sparse because often the locations cannot be accurately specified by the users. To overcome this data sparsity problem, this paper proposes UGC-LI, a user generate content driven location inference method to infer the location where users and social texts are created. The method can provide supporting data for location-based personalized information services. A probability model is constructed by comprehensive considering the distribution of location words and social graph of users via local words extracted from user generated texts to locate the users in multi-granularity. Further, a parameterized linguistic model based on location is presented to calculate the city where the tweet is published. The results of experiment on real-word dataset demonstrate that this new method outperforms existing algorithms. In the experiment, 64.2% of users are identified within 15km displacement distance, 81.3% of the living cities and 32.7% of the cities where the tweets were tweeted are correctly located.