基于性别的暴力在140个字符或更少：Twitter的# BigData案例研究
Gender-Based Violence in 140 Characters or Fewer: A #BigData Case Study of Twitter
摘要：
公共机构越来越依赖社交媒体网站的数据来衡量公众态度，并提供及时的公众参与。这种依赖包括探讨公众对基于性别的暴力等重要社会问题的看法。在这项研究中，我们研究了在十个月内从Twitter收集的近1400万条推文组成的大（社交）数据，以分析关于GBV的公众舆论，突出了地理位置和性别的推文实践的性质。我们展示了计算社会科学的效用，从语料库中挖掘洞察力，同时考虑到瞬态事件和社会文化因素的影响。我们揭示了公众对GBV容忍度的认识，并提出了干预机会和干预有效性的测量，帮助政府和非政府组织制定政策。
英文摘要：
Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development.
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