Data Critique

Welcome to the "Data Critique" section. We will explore the strengths, limitations, and ideological effects of our dataset. 

What can our dataset achieve?

Our dataset can illuminate the evolution of popular music in the USA from 1960 to 2010. It can provide insights into the representation of gender in popular music, dominant themes, and lyrical content across different decades, and correlations between the social and cultural events of a specific era and the types of music that dominated the Billboard Hot 100 during that time. The dataset can also highlight the popularity of songs and the influence of gender on this popularity. This might include the gender of the artists who are making the Billboard Hot 100 and whether there are any trends over time.

Our dataset allows for a comprehensive analysis of the musical genres that emerged and declined over the five-decade period. By examining the stylistic shifts and genre crossovers, we can gain a deeper understanding of the cultural and artistic influences that shaped the American music landscape. Moreover, the dataset sheds light on the sentiments expressed through popular music across different historical periods. Furthermore, we can investigate the relationship between the geographic origin of artists and the popularity of their songs, providing insights into regional musical preferences and trends throughout the United States.

What can't our dataset achieve?

While our dataset offers significaunt potential for analysis, it is also essential to recognize its limitation. Currently, our dataset did not specifically focus on the racial influence of popular music. We do not have a race column that helps us to identify the race of each singer between 1960-2010. Even though we have a gender filter, at this moment we do not have a race filter that allows our users to look at the racial influence on popular music. We’d hope to collect more data and add such a feature in the future.

Ideological Effects of Our Dataset's Ontology

The ontology of our dataset focuses on temporal classification, music characteristics and artist identification. The use of time categories, quarter/year/five-year period/year, imposes a linear perspective on the evolution of popular music. There is an underlying ideology that views music as a progressive or evolving phenomenon.

The dataset also pays special attention to the technical aspects of the tracks including harmonic and timbral topics, principal components of the harmonic and timbral topics and chord change counts. This highlights the ideology that news music is mainly an object of “scientific” analysis. It assumes that music’s essence and appeal can be quantified and understood through technical aspects. However, this may overlook the subjective and emotional aspects of music.

Our original dataset has identifiers for the artists but no important demographic details such as gender and ethnicity. This neglect may reflect an ideology that focuses only on the musical product which is detached from the artists’ identity or cultural/social roles. Therefore, it silences important discussions around representation, inclusivity and diversity in the music industry.

Our dataset exclusively focuses on songs that are in the Hot 100 and this may reflect an ideology that equates commercial success and mainstream view with musical values. We are trying to incorporate the gender of artists as a main category in our dataset. Once that is included in our new dataset, it may also suggest a potential ideological bias by prioritizing only the gender.