Abstract:
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The world is a polarized place due to people that hold firm averse opinions. In recent years, this subject has come to light due to the emergence of social media and divisive journalistic campaigns. To demonstrate the increasing level of polarity, we analyze daily news articles by expanding on a high-dimensional, temporal variant of the Latent Dirichlet Allocation (LDA) algorithm. This algorithm allows us to discern evolving daily topics and the intensity and accelerated rate that the topics enter and leave the media. Furthermore, the temporal LDA algorithm can be computationally expensive, so we implement techniques to improve the efficiency of the algorithm. Since the veracity of news sources are at times questionable, we address polarizing topics to determine the levels of agreement between these sources. To measure the polarization, we propose a new metric for assessing polarity between and within news sources and articles, based on entropy metrics. The performance of our temporal LDA algorithm and polarity metrics are compared with other competing methods. Through this work, we assess the nature and origins of various polarizing sources and evaluate their impact on society.
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