Detecting Trends in Community Engagement: At Wesleyan and Beyond

When it comes to activism and community service, Wesleyan has always tried to stay ahead of the curve. But this can be difficult, as the concerns and trends of community engagement are constantly shifting. Often, new topics will seemingly erupt out of nowhere, and it will take a while for word to spread. There are so many existing concerns that it can be difficult for new voices to be heard and for old voices to catch on to the changes. It might seem as though the trends in community engagement are shifting constantly, without any pattern. But can technology detect one?

Wesleyan’s Text Mining class was assigned the task of investigating this dilemma. They were asked to analyze the relationship between approaches to community engagement in the past and what people desire from it in the future. For past data, they collected the text of old Argus articles tagged “community engagement.” These articles were meant to illuminate what kinds of activities were most popular. Present data was collected through focus groups that were asked about the current state of community engagement at Wesleyan and how it could be improved. From this data, class groups hoped to discover how much the current activities overlap with the desires of the focus groups, as well as identify which community engagement topics are popular and which ones are new.

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Crowdsourcing Data Analysis: The complexities of free data labor in a data hungry market

Companies don’t know where to look to find the data analysts they need. A February 2017 article reported that 40% of major companies are struggling to find reliable data analysts to hire. According to TechTarget, “a lack of skills remains one of the biggest data science challenges,” and many tech magazines have reported something similar. This has led to companies sponsoring campaigns encouraging people to learn coding and universities to create comprehensive data analysis training programs. But it has also led to the widespread use of crowdsourcing data analysis. Crowdsourcing, while not a new tool in data science, has recently become extremely popular as a way for companies to fulfill their data analysis needs, from gritty data cleaning to full blown model creation. Last month DataCrunch reported on Kaggle, a website that allows companies to host competitions with a dataset they need to be analyzed in some way. Another example is DrivenData, who do activism work themselves but have a similar competition layout that runs their projects. The way the competition model works is that the participant or group whose model is chosen as the best by the company receives a cash prize. However, these competitions get a large enough number of submissions that the chance of winning the prize is rather low.

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Photo of the day – April 14, 2016

With rainy days finally over, students from the Introduction to Text Mining course (QAC 386) decided to hold the class outside, which they successfully did on the lawn near Allbritton Hall.  The topic of the day was tree parsing using openNLP package in R.

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In the photo, left to right: first row: Trisha Arora ’16, Taran Carr ’16, Antonio Robayo ’16, and Jack Trowbridge ’16; second row: Grace Wong ’18, third row: Sara Eismont ’18.