A Lyrical Analysis of Chart Topping Rap Songs 1987-Present Part 3

To conclude this three part series, I will attempt to use the collected data to tease out some themes from each decade of rap music, and then compare the beginnings of modern rap to its incarnation in today’s popular culture. Let’s start with the 1980s, rap’s first exposure to popular culture and media attention. The two most popular words of the 1980s were “You” and “Me”. This is interesting because all other decades of rap that I analyzed had the most popular words “I” and then “You”. So what this tells me, using a social lens, is that popular rap from the 80s focused on the rapper either telling us as listeners, or people rapped about in song, what we have to do or what we should do. Keeping the social focus, I can discern that 80s rap had a certain societal focus, as in the rapper wanted to tell “you” about what was going on and what “you” can do to help. A look at the popular verbs of the 80s helps us further this conclusion. The top three verbs were “Know”, “Love”, and “Fuck”. The high usage of the verb “know” leads me to believe that there was a focus on knowledge, either of socioeconomic problems or knowledge of a rapper’s lyrical prowess or style. “Love” as in “I love…” sounds pretty interesting but in reality it might just be a more descriptive version of “I like…” because it doesn’t sound quite as good in flow. “Fuck” is an interesting verb in context of the 80s and socially aware rap, hits such as NWAs “Fuck da Police” speak to a frustration with social disparities and social problems, usually with “fuck” an exclamation of frustration and disgust with current authorities or attitudes. As far as 1980s adult or profane language goes, it is pretty tame compared to later decades of rap. The top words in this category are as follows: “Fuck”, “Horny”, “Fight”, “Shit”,  and “Nigga”. “Fuck” has already been spoken to but let’s look at the other 4. “Horny” really can’t be taken any other way than talking about sexual exploits. “Fight” is an interesting one, and again using 1980s context can speak to larger issues, such as the desire to “Fight the Power” and Public Enemy eloquently said in their hit entitled so. “Shit” is often used to describe a bad situation, for example: “I hate this shit”. Again, this can be taken as a verbalized frustration that rap in the 1980s was characterized by. “Nigga” is popular in all decades of rap, but the 1980s see this as the only variation of the word in the top 10. As in all decades of rap, “nigga” is used to refer to the rapper’s compatriots, usually in a jovial or casual manner.

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A Lyrical Analysis of Chart Topping Rap Songs 1987-Present Part 2

Before I get started with this blog post, I’d like to inform the reader that this and subsequent posts will contain prolific language used in rap vernacular and is not intended to offend any person nor group of people. The use of such language is meant to keep the original intent and message of rap artists through this research. Use of such language will only happen in reference to such word, not in the analysis as descriptors nor derogatory terms.

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Single-Sided NMR of Paint Films After Climate Cycling: Conclusion

This will be my last Monroe blog post. Fortunately, the chemistry department gave me additional funding to continue research for the remainder of the summer. But as of now, here is where my research stands:

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A Lyrical Analysis of Chart Topping Rap Songs 1987-Present

To begin this research into changing themes of popular rap music, I had to compile a usable data set. To find the songs that I would use, I used the Billboard Hot Rap Songs chart, and looked through the historical data. Unfortunately, the data on Billboard only goes back as far as March 11, 1989. So, starting from this date I worked forward each week, checking to see what the #1 song was each time. From there, I gathered lyrics from online sources, such as Genius, for these songs and copied those into a text file for each new entry. The text files were “scrubbed” as it were, before moving on to the next song, so I would have to make sure the text files contained only lyrics, no artist names nor “repeat” notifications allowed as to preserve the integrity of the data. I moved on from March 11, 1989 through June 10, 2017. At this point, all text files have been made for those years, and I stand to “compile” large files for each decade and 1/3 of a decade, for comparison purposes and so that running the text through the algorithm isn’t completely mindless. To find popular rap songs for 1987 and 1988 (the years Billboard does not record) I had to peruse the Internet and consult various “Top 10” lists to get a general consensus of what the 10 best songs of the year were. Now, after these mild data collection difficulties, I stand to make text files for each 1/3 decade and also each decade. From there I can feed the data into the word frequency algorithm.

Part One: Comparing Xenophobia in US-American and German Social Media

Before I begin analyzing social media posts on a larger scale, it is important to build context for these posts. Therefore, I am beginning my research by reading about asylum/refugee policies, hate crimes, and outspoken critics of immigration in both the US and Germany. I have also begun sifting through the tweets of various users who display resentment towards perceived foreigners. I intend to conduct smaller-scale analysis of social media posts “by hand” before progressing to an automated process. This initial stage is important because it allows me to achieve more depth and accuracy than the automated language analysis will (especially considering how much sarcasm I have already encountered). Another goal of this process is to collect keywords, which I will use in the second stage to design search terms. The information I gather in the first stage will help me filter for xenophobic tweets in stage two.

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Jar Jar is the key to all of this: Part Three

This research has been fascinating both to conduct and discuss with friends and family. I strove to compose a reasonable case both for and against Lucas, and I wanted to add some final thoughts on the whole matter.

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Jar Jar is the key to all of this: Part Two

In my prior post, I endeavored to establish the case that the Prequels were offensive in their creation of characters who, though strange aliens, represented clear stereotypes of Asians, Jews (or Arabs), and African-Americans. In this post, I will attempt to construct a counter-argument. For simplicity, I will begin once more with the Neimoidians.

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Jar Jar is the key to all of this: Part One

George Lucas once infamously declared, “Jar Jar is the key to all of this.” In a discussion of race in Star Wars, I’m inclined to agree that Jar Jar Binks is key. I’ve spent the past few weeks delving into the complexities of the Star Wars Universe. Some days have been marvelous (watching the original trilogy on VHS stands out) while others have proven grueling (The Phantom Menace twice in two days was no cakewalk). To keep the discussion manageable, I’m limiting my focus to the prequels (Episodes I-III), although I will at times make mention of the original trilogy and other relevant information/fan theory (yes, Darth Jar Jar deserves some attention). The format of my entries will be sequential. I will first argue that the prequels contain racist, historic stereotypes through the presentation of certain alien creatures. Then, I will counter with a defense of Lucas. I will close with a reflection on the project as a whole and some of the broader lessons I found myself learning as I examined the characters of a galaxy far, far away.

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The Literature Behind Milk Stations and GIS

Before I start my work using GIS, I have spent time working with Professor Brian Beach to get a deeper understanding of how others have completed similar work to what we plan on doing. Before coming to campus, I read a paper on calculating infant mortality rates in the early 20th century by state, which focused on the difference between the mortality rates for white and black Americans. While the racial difference does not pertain to my research, the information on how mortality rates can be incorrect because of underreporting of births is important. Later in the project, when actual mortality rates are going to be calculated, this kind of bias could really affect the project. Fortunately, the paper discussed the accuracy of larger northern cities and Boston in particular as far as birth reporting is concerned, so it should be less of a problem.

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Bienvenido en Espana

Near the end of the spring semester, I piloted my survey in English on peers at William and Mary to find any glitches there might be. There were many. However, with those sorted out, I took every free second of three days and translated the survey into French and Spanish in preparation to send them out to citizens of these countries. Having fine-tuned the English one as well, I began to send it to participants: citizens of Virginia Beach 18 and older. My participant pool right now comprises contacts and coworkers of my parents, to get some semblance of sampling (because they work VERY different jobs) and I will add other contacts for numbers as I see fit.

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