The #Holidays, According to Twitter

The holiday season is among us, and people are flocking to social media to share their thoughts about the holidays with friends, family, and followers. Using Nvivo, a text-analysis software, DASIL tracked tweets with the hashtags #Kwanzaa, #Hanukkah, and #Christmas published on Dec. 10th and earlier to create word clouds that demonstrate the top 100 words most frequently associated with each holiday.
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For Christmas, tweets highlight how highly commercialized the nature of the holiday has become. The most common words found alongside “Christmas” include “win,” “giveaway,” “luck,” “enter,” “chance,” and others, attesting to how advertisements for giveaways and contests to win prizes are dominating talk of Christmas in the Twittersphere. A handful of tweets reference the holiday’s secular traditions, with words like “decked,” “festive,” “tree,” and “lighting.”

Like Christmas tweets, Hanukkah tweets mention words relating to its traditions (“menorah,” “candles,” “lighting,” and “latkes”). Hanukkah tweets differ from Christmas tweets by centering instead on sentimental values and inclusivity, with words like “happy,” “everyone,” “celebrating,” and “family.” Some Hanukkah tweeters are also using the holiday to highlight political issues, with mentions of “Netanyahu,” Israel’s prime minister, as well as “police,” “western,” “border,” and “#muslim.” Surprisingly, both Hanukkah and Christmas tweets completely ignore their religious roots, with no mentions of words relating to the respective origins of each holiday.

The words associated with Kwanzaa, the week-long celebration of African heritage by the West African diaspora, are a grab-bag: words like “celebration,” “promoted,” “happy,” “family,” and “submissions” reveal a mixture of sentimental and commercial connotations. Interestingly, the “#blacklivesmatter” hashtag crops up as one common word associated with Kwanzaa, showing how Kwanzaa, like Hanukkah, is being used as a vehicle to spotlight social and political issues revolving around racial injustice.

All in all, the big takeaways from this short text analysis are that, of the three current holidays, Christmas is the most commercial.  Hannukkah celebrates family and inclusivity, but also has a political edge, while Kwanzaa has a less focused identity.

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Highlighting the Importance of Intersectionality in the Gender Pay Gap

The gender pay gap is again receiving much-needed publicity in recent years as a topic of debate between US presidential hopefuls for 2016 and information uncovered from Sony’s email hack this time last year. While the phrase “women get paid 78% of what men are paid” is touted frequently in discussion, the 78% figure is static in dimension. Do all women get paid 78% of what men are paid, or is it just a subset of the female working population?

There is a lot more to the 78% figure than meets the eye, and the intersection of race and gender is important to telling the fuller story behind the 78%, and the wider issue of gender parity in earnings.

Using DASIL’s Pay by Race & Gender visualization, we can see that race plays a significant role in the pay of a full-time working woman and reveals the nuances to the widely-cited 78% figure. Asian women working full-time in the US are (and have been) the subset of women getting paid closest to what all men are getting paid throughout history, at 86% that of men in 2013. However, Asian women were only paid 75% that of Asian men in 2013. On the other end of the spectrum, Hispanic women were disproportionately getting paid only 60% of men’s wages in 2013, the lowest of all recorded races. Hispanic males also earn the lowest in comparison to all men, at 64% of what all men earn (not shown) in 2013. As the graph indicates, the asymmetric trends for Hispanic and Black women have remained relatively constant for the past twenty years.

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With regard to part-time labor, however, there is virtually complete gender parity in 2013 when focusing on average figures, with “all women” receiving 99% of what a man earns. When filtering by race, part-time working White and Asian women even get paid more than that of average men; white women receive 106% of what a man earns, and Asian women 101% in 2013. However, racial disparity still persists: both Black and Hispanic women in part-time labor received 85% of what part-time men were paid in 2013, and the closest Black and Hispanic women have been in achieving pay parity with the average man was in 1994.

As this infographic suggests, one reason for full-time and part-time pay disparity can be due to industry: black women are more likely to work in less-lucrative jobs (e.g. service, healthcare) than high-lucrative jobs (e.g. STEM, management). Relatedly, education can be a contributing factor: Hispanic and Black women are less likely to graduate than whites. Yet, even if women of color have the same education levels as their white peers, they are still paid less; there is more contributing to pay disparity than the educational attainment of women of color.

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While there is clear cause for more work to be done in bridging the pay gap between men and women, recognizing the multiple dimensions of the issue will be key to creating meaningful and effective policy changes.

Explore more trends with our Pay by Gender and Race visualization here.

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Visualizing Mass Communications and State Institutions in Wartime China (1937-45)

In China, the study of history has always gone hand-in-hand with the study of geography. When studying China’s modern history, however, focus has shifted toward large-scale processes, such as revolution, and large-scale sociological transformations, such as changing class relations. More recently, however, some historians are starting to bring geography back in. Pathbreaking endeavors such as the China Historical GIS project and Harvard University WorldMap platform-based ChinaMap allow researchers to visualize the transformation of China across space and time. The result has been a new understanding of China and Chinese history highlighting the spatial distribution of ethnic and linguistic diversity, economic development, elite networks, and state institutions. One exciting result of this new understanding is that it allows students and researchers alike to visualize large-scale processes across time periods, which can in turn lead to new questions about how different places might have experienced the same era or event. Through the use of spatial approaches, we are challenged to rethink the applicability of national historical narratives to local human landscapes.

As a teacher and researcher of East Asian history, much of what I do focuses on how media, institutions, and person-to-person networks have connected the modern Chinese state to populations both inside and outside of China. Working in tandem with DASIL, I have begun to build and visualize datasets which describe how the “connective tissue” of state-building looked during the period of China’s War of Resistance to Japan (1937-1945)—a period of intense destruction and dislocation which some historians have also described as key period of modernization. This data is drawn from two editions of The China Handbook: a publication of the Chinese Ministry of Information released in 1943 and again in 1946. I discovered this publication quite by happenstance while searching the Grinnell College Library collections for local gazetteer data related to the period of China’s Republican Era (1911-1949). The value of The China Handbook is that it provides comprehensive provincial and urban data for a number of indicators of state development; here we (myself and DASIL’s outstanding post-bac fellow, Bonnie Brooks ’15) have focused on data concerning communications, education, and health care. To be fair, and as admitted by The China Handbook’s original editor, Hollington K. Tong, this data is not exhaustive, nor is it necessarily reliable given the rapidity of changes brought about by war and resulting partition of China into competing political zones. It does, however, represent at least a starting point for visualizing what China’s wartime states looked like “on the ground,” viewed through the lens of communications and other institutional infrastructure.

Below the level of national boundaries, modern China is divided into numerous separate administrative units known as provinces. However, the number of provinces has changed with time and successive governments, which poses a challenge for those seeking to visualize data at the province level for eras during which the number of these units was larger than it is today—as was the case during the latter half of the Republican Era, which witnessed a proliferation of efforts to tame China’s restive and geopolitically fragile borders through the process of province-building. A key part of Bonnie’s contribution, then—the results of which will hopefully be used and refined by other researchers working at the intersection of geographic information systems (GIS) and modern Chinese history—was the creation of new shapefiles corresponding to each province that existed during the 1937-1945 period. The resulting maps are thus entirely new creations, and will hopefully serve to help bridge the current gap which lies between geospatial research on imperial China and research on contemporary China after Mao.  The shapefiles are available for download in DASIL’s Downloadable Data section.

For the map:

    • The Contents button(contentsbutton) will display all layers. Unclick the checkbox next to the layer name to hide the layer. To view the legend, click on the “Show Legend” icon (contentsbutton) below the layer name.
    • To examine other variables, find the “Change Style” button (contentsbutton) below the layer name you wish to view, then select the desired variable from the “Choose an attribute to show” drop-down menu.  You may alter the map with colors, symbols or size. You may also alter variables (e.g. normalize variables by population).
    • Click on an individual Chinese province to see available data.
    • The shapefiles featured in the map are available for download on the DASIL website. Click here for the download.

 

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