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As discussed in the previous reflections, data mining, text analysis, sentiment analysis and now topic modeling a contributing in unimaginable ways. These advancements in the data modeling world have expanded its impact in the world of humanities — specifically literature. Using Topic Modeling, computers are able to recognize patterns, themes, and keywords in texts. This has allowed for better research and analysis on human society in many different ways. Data’s ability to solve issues is no longer constrained to numbers. Today computers can crack codes, analyze speeches and extract key elements in literature at rates much faster and efficient as humans did previously. In the study “Where, What, When and Sometimes Why: Data Mining two decades of Women’s history” uses these skills to recognize gender inequality in literature of the past and current article publication rates. Using “word frequency”, computers run programs on finding the frequency of words such as “he”, “him”, “his”, “she”, “her” and “hers”. The results showed that male pronouns were used significantly more often than the female pronouns, which clearly alludes to some question about gender equality in all aspects (considering it’s coming from literature). NB discusses the pattern of white NFL players more frequently being called “intelligent” and black players more frequently being called “natural”. He writes: “studies like the one in the article are easy to conduct, but are they completely relevant and accurate in their findings?” He further discusses the potential of this trend to start off with because there are different quantities of black and white players in the NFL on each team across the team. The same may apply to today’s study. Since women were less represented in academia, the further we go back in time, some of the data we collect today on the literature of the past may not perfectly reflect the gender inequality in today’s society. In retrospect, it’s important to how exactly the topic modeling program is being run to know what exactly the results are saying.