While analyzing the graphs, we found that our data as a group had strong similarities in some graphs, but had significant differences in other graphs. Overall, we found that people used my hashtag to resist Trump while people used Tim’s hashtag (#makeamericagreatagain) and Harrison’s hashtag (#2a) to express support for Trump and the conservative ideology. We were most similar in the text analysis. Tim, Harrison, and I had very politicized word graphs and words related to Trump were the most used. The text analysis revealed that people used Trump as a figure or icon to express their political views on the election, immigration ban, and the Second Amendment. Our social network analysis graphs showed the varying levels of connection that our groups had. People who tweeted #makeamericagreatagain (Tim) were not connected at all while people who tweeted #nobannowall (Jennifer) were very well connected even when they had different viewpoints on the issue. Harrison (#2a) described his SNA as very clique since he had many clusters of connections, but people were not all connected. We used different words to analyze the word graphs but the most politicized words such as Trump grew popularity as the media covered our topic more.
We had minor variations in other graphs. They indicated to us how international our tweets spread. Most of our tweets were written in English, but our other languages varied. Both Tim and my tweets consisted of insignificant uses of 8-10 other languages while most of Harrison’s foreign language tweets were written in Russian. It perhaps signifies that Tim and my topics are more international and Harrison’s hashtag is also international, but more regionalized. We used different days for our tweets (and Harrison used tweets per hour) so it was hard to compare the popularity of our tweets within the same time period. But our tweets peaked after a media source discussed our topic more or a physical event (such as a protest) happened. We did not have enough location data, but a majority of our users were based in the U.S. I had the most tweets abroad.
Overall, my graphs showed me that Twitter users used my hashtag to criticize Trump’s immigration policies and critics attempted to reach out to his personal account @realdonaldtrump to vocalize their opinions. While other hashtags did not generate conversation within different groups, people who used my hashtag talked to others who opposed their views to generate attention. I think that my word graph and SNA articulated this conclusion the best. My word graph showed people used anti-Trump phrases such as “resist,” “resistance” and “impreachtrump” the most along with my hashtag. My SNA showed that every group conversed with one another even though people who used more conservative hashtags did not.
Presentation Outline:
1. Title slide – introduce myself (5 seconds)
2. State the hashtag #nobannowall, briefly what does it relate to including the Mexican wall and immigration ban (35 seconds)
3. Key argument for word graph: Twitter users used my hashtag to criticize Trump’s policies and the popularity heightened when Trump proposed an anti-immigration policy (50 seconds).
4. Key argument for SNA: Twitter users connected with one another to develop their arguments against the Trump policy and tried to get the attention of Trump himself. The conversation on the issue is not isolated to different ideological groups (50 seconds).
5. Refer to: Cheney-Lippold, John. 2011. “A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control.” Theory, Culture & Society 28 (6): 164–81. So what happens if in the future, you are seen as connected to Trump even when you actually tweeted him to criticize his policies (1 minute)?
JTran, what a year its been! This may be my final comment, so way to go! Great insight as always, looking forward to your presentation monday!
I am very curious to see how your argument for the SNA develops Jennifer, especially after seeing your graph and how connected everyone is. Maybe you could bring in the tweet frequency a bit when talking about your word graph and the spikes and lulls of tweets around his immigration policy. I can’t wait to see your presentation on Monday!
Your key argument should be this point: “While other hashtags did not generate conversation within different groups, people who used my hashtag talked to others who opposed their views to generate attention.” This is unique and worth revealing. The first sentence re the topic of the data just describes the hashtag, while the above is the key findings.
Good use of Cheney-Lippold!