Purpose

Social media data contains a wealth of content related to customers’ reactions to, and comments on, firms’ performance. Through the lens of signaling theory, this paper aims to investigate the use of social media data as a knowledge resource in communicating firms’ noncompliance risk to regulatory agencies.

Design/methodology/approach

This paper proposes a two-step social media analytics framework to detect noncompliant firms. First, it creates a context-specific dictionary that contains keywords relevant to firms’ noncompliant behaviors. Next, it extracts those keywords from customer reviews, customer sentiment and emotions to predict firm noncompliance. It tests these ideas in the context of food safety regulations.

Findings

It identified over 100 words that are related to restaurants’ hygiene deficiencies. Using the occurrence of these words in customer reviews, as well as sentiments and emotions expressed within them, the author’s best-performing model can identify nearly 90% of the restaurants that severely violated regulations.

Practical implications

After being processed by appropriate machine learning algorithms, customer reviews serve as valuable knowledge resources, enabling regulatory agencies to identify noncompliant firms. Regulatory agencies can use this model to complement the current compliance monitoring scheme.

Originality/value

This research contributes a novel methodology for creating a context-specific dictionary that keeps only the relevant words customers use when discussing firms’ noncompliant acts. In the absence of such an approach, numerous irrelevant signals would be included in the modeling process, thereby increasing the cost of social media analytics.

中文翻译：

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在知识管理中利用社交媒体数据来识别不合规情况：来自餐饮服务行业的洞察

 目的

社交媒体数据包含大量与客户对公司绩效的反应和评论相关的内容。通过信号理论的视角，本文旨在研究使用社交媒体数据作为知识资源向监管机构传达公司的不合规风险。

设计/方法/方法

本文提出了一个两步社交媒体分析框架来检测不合规的公司。首先，它创建一个特定于上下文的字典，其中包含与公司的不合规行为相关的关键字。接下来，它从客户评论、客户情绪和情绪中提取这些关键字，以预测公司的不合规情况。它在食品安全法规的背景下测试了这些想法。

 发现

它确定了 100 多个与餐厅卫生缺陷相关的词。利用这些词在客户评论中出现的情况，以及其中表达的情绪和情绪，作者表现最好的模型可以识别出近 90% 严重违反规定的餐厅。

 实际意义

客户评论经过适当的机器学习算法处理后，可作为宝贵的知识资源，使监管机构能够识别不合规的公司。监管机构可以使用此模型来补充当前的合规性监控计划。

 原创性/价值

这项研究提供了一种创建特定于上下文的词典的新方法，该词典仅保留客户在讨论公司的不合规行为时使用的相关词语。在没有这种方法的情况下，建模过程中将包含许多不相关的信号，从而增加社交媒体分析的成本。