Diving into Robocall Content with SnorCall
Diving into Robocall Content with SnorCall
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发表时间：
2023
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通讯作者：
S. Prasad;T. Dunlap
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作者：
S. Prasad;T. Dunlap
未经请求的批量电话-被称为“机器人电话”-几乎超过合法电话，压倒了电话用户。虽然这些电话中的绝大多数是非法的，但它们也是短暂的。尽管电话服务提供商、监管机构和研究人员可以随时访问呼叫元数据，但他们没有工具来调查所需的大规模呼叫内容。本文介绍了SnorCall，一个可扩展和有效地从robocalls中提取内容的框架。SnorCall利用Snorkel框架，允许领域专家编写简单的标签函数来对文本进行高精度分类。我们将SnorCall应用于23个月内收集的232，723个robocalls的成绩单语料库。在许多其他调查结果中，SnorCall使我们能够获得关于不同诈骗和合法robocall主题的流行程度的初步估计，确定这些电话中引用了哪些组织，估计诈骗电话中请求的平均金额，确定竞选活动之间的共享基础设施，并监控与选举相关的政治电话的上升和下降。因此，我们展示了监管机构、运营商、反robocall产品供应商和研究人员如何使用SnorCall来获得对robocall内容和趋势的强大而准确的分析，从而实现更好的防御。
Unsolicited bulk telephone calls — termed "robocalls" — nearly outnumber legitimate calls, overwhelming telephone users. While the vast majority of these calls are illegal, they are also ephemeral. Although telephone service providers, regulators, and researchers have ready access to call metadata, they do not have tools to investigate call content at the vast scale required. This paper presents SnorCall, a framework that scalably and efficiently extracts content from robocalls. SnorCall leverages the Snorkel framework that allows a domain expert to write simple labeling functions to classify text with high accuracy. We apply SnorCall to a corpus of transcripts covering 232,723 robocalls collected over a 23-month period. Among many other findings, SnorCall enables us to obtain first estimates on how prevalent different scam and legitimate robocall topics are, determine which organizations are referenced in these calls, estimate the average amounts solicited in scam calls, identify shared infrastructure between campaigns, and monitor the rise and fall of election-related political calls. As a result, we demonstrate how regulators, carriers, anti-robocall product vendors, and researchers can use SnorCall to obtain powerful and accurate analyses of robocall content and trends that can lead to better defenses.