Tweets: Airbnb from Twitter

Sharing a set of 6 geographical regions datasets. Kindly refer to the below article for further explanation. Title of paper: Viewing Airbnb from Twitter: factors associated with users' utilization Abstract: Airbnb is a peer-to-peer accommodation website in the sharing economy. Past studies have examined the factors associated with Airbnb utilization from various platforms, but not exclusively from Twitter. A total of 21,097 tweets was collected in a period of two months, and the tweets were qualitatively analyzed with the help of text analysis tools to verify the discourse of discussion. Literature was reviewed for common factors attracting clients to an Airbnb accommodation. Factors were then qualitatively analyzed and compiled using Wmatrix, and the themes that emerged were: Price and status, social interaction and communication, location, reputation, amenities and a pet-friendly environment. This result provides a deeper insight to Airbnb hosts to strategize and add value to their current market situations. The published paper is at https://dl.acm.org/doi/10.1145/3428757.3429112 Phoey Lee Teh, Yeh Ching Low, and Pei Boon Ooi. 2020. Viewing Airbnb from Twitter: factors associated with users' utilization. In Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services (iiWAS '20). Association for Computing Machinery, New York, NY, USA, 274–281. DOI:https://doi.org/10.1145/3428757.3429112本数据集共享6个地理区域数据集。详细说明请参阅下述学术文献。 论文标题：《从推特（Twitter）视角观察爱彼迎（Airbnb）：影响用户使用行为的相关因素》 摘要：爱彼迎（Airbnb）是共享经济领域的点对点住宿共享平台。过往研究已从多平台维度探究了影响爱彼迎用户使用行为的相关因素，但尚未专门聚焦于推特（Twitter）平台。本研究于两个月的周期内共收集到21097条推文，并借助文本分析工具对推文开展质性分析，以验证相关讨论话语。研究首先通过文献梳理总结出吸引用户选择爱彼迎住宿的常见影响因素，随后使用Wmatrix工具对这些因素进行质性分析与整合，最终提炼出六大核心主题：价格与身份标识、社交互动与沟通、住宿位置、口碑评价、配套设施以及宠物友好环境。本研究结果可为爱彼迎房东制定运营策略、优化当前市场经营状况提供更具深度的参考依据。 该已发表论文可通过以下链接获取：https://dl.acm.org/doi/10.1145/3428757.3429112 作者：Phoey Lee Teh、Yeh Ching Low与Pei Boon Ooi，2020年。《从推特（Twitter）视角观察爱彼迎（Airbnb）：影响用户使用行为的相关因素》，收录于第22届国际信息集成与Web应用服务国际会议（iiWAS '20）论文集，美国计算机协会（Association for Computing Machinery），美国纽约，第274-281页。DOI: https://doi.org/10.1145/3428757.3429112