Assessing Social License to Operate from the Public Discourse on Social Media
Assessing Social License to Operate from the Public Discourse on Social Media
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DOI：
10.18653/v1/2020.coling-industry.14
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Chang Xu;Cécile Paris;R. Sparks;S. Nepal;Keith Vander Linden
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文献类型：
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作者：
Chang Xu;Cécile Paris;R. Sparks;S. Nepal;Keith Vander Linden
组织正在越来越定期地监控其社交运营许可证(SLO)。SLO是组织从公众那里获得的支持程度，通常通过调查或焦点小组进行评估，这需要昂贵的人工努力，并产生很快过时的结果。在本文中，我们提出了SIRTA(Social Insight via Real-time Text Analytics)，这是一个新的实时文本分析系统，通过分析社交帖子中的公共话语来评估和监控组织的SLO水平。为了评估SLO水平，我们的洞察力是提取人们对组织的立场，并将其转化为SLO水平。SIRTA通过执行三个文本分类任务链来实现这一点，其中它识别与任务相关的社交帖子，发现帖子中讨论的关键SLO风险，并推断特定于SLO风险的立场。我们利用最新的语言理解技术(例如，BERT)来构建我们的分类器。为了随着时间的推移监测SLO水平，SIRTA使用质量控制机制来可靠地识别SLO趋势和市场中多个组织的变化。它们是基于指数加权移动平均(EWMA)计算得到的SLO水平的平滑时间序列。我们的实验结果表明，SIRTA能够高效地从社交帖子中提取立场，用于SLO水平评估，并且SIRTA提供的对SLO水平的持续监测使关键SLO变化能够及早发现。
Organisations are monitoring their Social License to Operate (SLO) with increasing regularity. SLO, the level of support organisations gain from the public, is typically assessed through surveys or focus groups, which require expensive manual efforts and yield quickly-outdated results. In this paper, we present SIRTA (Social Insight via Real-Time Text Analytics), a novel real-time text analytics system for assessing and monitoring organisations’ SLO levels by analysing the public discourse from social posts. To assess SLO levels, our insight is to extract and transform peoples’ stances towards an organisation into SLO levels. SIRTA achieves this by performing a chain of three text classification tasks, where it identifies task-relevant social posts, discovers key SLO risks discussed in the posts, and infers stances specific to the SLO risks. We leverage recent language understanding techniques (e.g., BERT) for building our classifiers. To monitor SLO levels over time, SIRTA employs quality control mechanisms to reliably identify SLO trends and variations of multiple organisations in a market. These are derived from the smoothed time series of their SLO levels based on exponentially-weighted moving average (EWMA) calculation. Our experimental results show that SIRTA is highly effective in distilling stances from social posts for SLO level assessment, and that the continuous monitoring of SLO levels afforded by SIRTA enables the early detection of critical SLO changes.