Divesting in Socially (Ir)responsible Internet Service Providers

Emily Huff, Aaron Gember
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Abstract

Divesting in a company based on their social practices is one tactic for encouraging a company to alter their behavior. Individuals and institutions can divest in "socially irresponsible" ISPs directly by refusing to peer with or purchase transit from these ISPs, or indirectly by avoiding paths through these ISPs. While the economics of divesting in ISPs and mechanisms for exerting more control over Internet paths have been previously studied, a major challenge remains: how do individuals and institutions assess whether an ISP is socially responsible? Large institutions may have the resources to research which ISPs are socially (ir)responsible, but most individuals and institutions lack these resources, thereby limiting their ability to effect social change. We address this challenge by designing a system for automatically assessing ISPs along various dimensions of social responsibility---e.g., environmental stewardship, censorship actions, or net neutrality practices. In particular, we gather publicly accessible web pages which discuss a particular ISP and social issue and apply stance detection---a natural language processing technique for determining a subject's attitude or commitment toward an object---to compute a "socially responsibility" score for individual ISPs and dimensions of social responsibility. We construct a data set over 600 web pages relating to net neutrality and 13 ISPs of varying sizes, and show our framework achieves 71% accuracy in determining an ISP's stance.
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剥离对社会(Ir)负责的互联网服务提供商
根据公司的社会实践来撤资是鼓励公司改变其行为的一种策略。个人和机构可以直接通过拒绝与这些互联网服务提供商对等或从这些互联网服务提供商购买中转服务来摆脱“社会不负责任”的互联网服务提供商,或间接通过避免通过这些互联网服务提供商的路径。虽然剥离互联网服务提供商的经济学和对互联网路径施加更多控制的机制已经被研究过,但一个主要的挑战仍然存在:个人和机构如何评估互联网服务提供商是否具有社会责任?大型机构可能有资源来研究哪些互联网服务提供商负有社会(ir)责任,但大多数个人和机构缺乏这些资源,从而限制了他们影响社会变革的能力。为了应对这一挑战,我们设计了一个系统,根据社会责任的各个方面自动评估互联网服务提供商。环境管理、审查行动或网络中立实践。特别是,我们收集了讨论特定ISP和社会问题的可公开访问的网页,并应用立场检测(一种用于确定主体对对象的态度或承诺的自然语言处理技术)来计算单个ISP和社会责任维度的“社会责任”分数。我们构建了超过600个与网络中立性和13个不同规模的ISP相关的网页的数据集,并显示我们的框架在确定ISP立场方面达到了71%的准确性。
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