An Information Nutritional Label for Online Documents

Abstract

With the proliferation of online information sources, it has become more and more difficult to judge the trustworthiness of news found on the Web. The beauty of the web is its openness, but this openness has lead to a proliferation of false and unreliable information, whose presentation makes it difficult to detect. It may be impossible to detect what is “real news” and what is “fake news” since this discussion ultimately leads to a deep philosophical discussion of what is true and what is false. However, recent advances in natural language processing allow us to analyze information objectively according to certain objective criteria (for example, the number of spelling errors). Here we propose creating an “information nutrition label” that we can automatically generated for any online text. Among others, the label provides information on the following computable criteria: factuality, virality, opinion, controversy, authority, technicality, and topicality. With this label, we hope to help readers make more informed judgments about the items they read.