Trends are those keywords, phrases, or names that are mentioned most often on social media or in news in a particular timeframe.They are an effective way for human news readers to both discover and stay focused on the most relevant information of the day. In this work, we consider trends that correspond to an entity in a knowledge graph and introduce the new and as-yet unexplored task of identifying other entities that may help explain the "why" an entity is trending. We refer to these retrieved entities as contextual entities. Some of them are more important than others in the context of the trending entity and we thus determine a ranking of entities according to how useful they are in contextualizing the trend.We propose two solutions for ranking contextual entities. The first one is fully unsupervised and based on Personalized PageRank, calculated over a trending entity-specific graph of other entities where the edges encode a notion of directional similarity based on embedded background knowledge. Our second method is based on learning to rank and combines the intuitions behind the unsupervised model with signals derived from hand-crafted features in a supervised setting. We compare our models on this novel task by using a new, purpose-built test collection created using crowdsourcing. Our methods improve over the strongest baseline in terms ofPrecision at 1 by 7% (unsupervised) and 13% (supervised). We find that the salience of a contextual entity and how coherent it is with respect to the news story are strong indicators of relevance in both unsupervised and supervised settings.

The central idea of this paper is to gain a deeper understanding of song lyrics computationally. We focus on two aspects: style and biases of song lyrics. All prior works to understand these two aspects are limited to manual analysis of a small corpus of song lyrics. In contrast, we analyzed more than half a million songs spread over five decades. We characterize the lyrics style in terms of vocabulary, length, repetitiveness, speed, and readability. We have observed that the style of popular songs significantly differs from other songs. We have used distributed representation methods and WEAT test to measure various gender and racial biases in the song lyrics. We have observed that biases in song lyrics correlate with prior results on human subjects. This correlation indicates that song lyrics reflect the biases that exist in society. Increasing consumption of music and the effect of lyrics on human emotions makes this analysis important.