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Understanding how ideas relate to each other is a fundamental question inmany domains, ranging from intellectual history to public communication.Because ideas are naturally embedded in texts, we propose the first frameworkto systematically characterize the relations between ideas based on theiroccurrence in a corpus of documents, independent of how these ideas arerepresented. Combining two statistics --- cooccurrence within documents andprevalence correlation over time --- our approach reveals a number of differentways in which ideas can cooperate and compete. For instance, two ideas canclosely track each other's prevalence over time, and yet rarely cooccur, almostlike a "cold war" scenario. We observe that pairwise cooccurrence andprevalence correlation exhibit different distributions. We further demonstratethat our approach is able to uncover intriguing relations between ideas throughin-depth case studies on news articles and research papers.

Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts