Difference between revisions of "Team:Minnesota/Facebook"
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<b><font size="4"><center> ...To the internet! </font></b><br></center><br> | <b><font size="4"><center> ...To the internet! </font></b><br></center><br> | ||
− | This is not a trivial issue. Why do people share certain pieces of information over others? To study this question, we turn to the largest social media site on the web: Facebook. With 1.19 billion active users last quarter on this networking website, the trends in sharing and liking can give us insight into what draws people to certain information. Using a Facebook tool to computationally extract article titles, likes, shares, and comments from major news sources to try to identify patterns in the trait informally known as "click-bait", or content designed to attract visitors and attention to a particular source. The title of a link or post often the only major decider in whether someone will click. | + | This is not a trivial issue. Why do people share certain pieces of information over others? To study this question, we turn to the largest social media site on the web: Facebook. With 1.19 billion active users last quarter on this networking website, the trends in sharing and liking can give us insight into what draws people to certain information. Using a Facebook tool to computationally extract article titles, likes, shares, and comments from major news sources to try to identify patterns in the trait informally known as "click-bait", or content designed to attract visitors and attention to a particular source. The title of a link or post often the only major decider in whether someone will click.<br><br> |
<b><font size="4"><center> Where we looked </font></b><br></center><br> | <b><font size="4"><center> Where we looked </font></b><br></center><br> | ||
− | With 24,462 New York Times posts, 13,758 Buzzfeed posts, and 14,069 Millions Against Monsanto (a leading anti-biotechnology group) posts, we sought to quantify linguistic metrics of these articles relative to "like" total. Using the NLTK and TextBlob modules of Python, we were able to analyze each title for their syntactically element and their sentiment and polarity. Additionally, we could crudely estimate emotional content by using the AFINN emotional lexicon, a dictionary of the most common emotional words with an associated magnitude. This gave us an array of metrics to try and estimate "likability" against. | + | With 24,462 New York Times posts, 13,758 Buzzfeed posts, and 14,069 Millions Against Monsanto (a leading anti-biotechnology group) posts, we sought to quantify linguistic metrics of these articles relative to "like" total. Using the NLTK and TextBlob modules of Python, we were able to analyze each title for their syntactically element and their sentiment and polarity. Additionally, we could crudely estimate emotional content by using the AFINN emotional lexicon, a dictionary of the most common emotional words with an associated magnitude. This gave us an array of metrics to try and estimate "likability" against.<br><br> |
<b><font size="4"><center> Our results </font></b><br></center><br> | <b><font size="4"><center> Our results </font></b><br></center><br> | ||
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+ | <b><font size="4"><center> Healthy communication = Healthy relationships </font></b><br></center><br> | ||
− | Finally, we return to biotechnology and how the scientists should talk to the public. Let's consider Millions Against Monsanto as our test group, which draws much of its following from those vividly fearful articles against GMOs. We can characterize the principle components of the news source, and design a title that others following this group would be more likely to click and possibly like. With the red bars in the figure below corresponding to negative correlation and blue corresponding to positive correlation, we can extrapolate this to decisions we would make when crafting articles representing our views. Lower the | + | Finally, we return to biotechnology and how the scientists should talk to the public. Let's consider Millions Against Monsanto as our test group, which draws much of its following from those vividly fearful articles against GMOs. We can characterize the principle components of the news source, and design a title that others following this group would be more likely to click and possibly like. With the red bars in the figure below corresponding to negative correlation and blue corresponding to positive correlation, we can extrapolate this to decisions we would make when crafting articles representing our views. Lower adjective count and lower verb count in titles of articles are correlated to a higher number of likes. The power of this tool is limited only by our ability to target the correct demographics. With it, we hope to utilize what has proven effective in reaching the public in order to return science and reason to the evolving conversation about synthetic biology. |
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Latest revision as of 03:41, 19 September 2015