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filterCoven Unit 3 Independent Study
Research Question
What worldviews dominate the Common Core discourse, based on sentiment analysis of bios of the major actors in the network?
Import Data
Wrangle Data
# A tibble: 1,168 × 5
sender target created_at text descr…¹
<chr> <chr> <dttm> <chr> <chr>
1 InnerScholars WeAreTeachers 2021-02-27 17:54:35 "@WeAreTeachers Ano… 💡Foun…
2 InnerScholars Bored_Teachers 2021-02-27 17:54:03 "@Bored_Teachers An… 💡Foun…
3 InnerScholars CommonSenseEd 2021-02-27 17:53:42 "@CommonSenseEd Ano… 💡Foun…
4 InnerScholars CommonSenseEd 2021-02-27 17:53:32 "@CommonSenseEd Ano… 💡Foun…
5 InnerScholars <NA> 2021-02-27 17:53:08 "Another form of pr… 💡Foun…
6 InnerScholars <NA> 2021-02-23 18:36:27 "Ever wanted to sho… 💡Foun…
7 InnerScholars CommonSenseEd 2021-02-24 17:25:29 "@CommonSenseEd Eve… 💡Foun…
8 InnerScholars Bored_Teachers 2021-02-24 17:24:50 "@Bored_Teachers Ev… 💡Foun…
9 InnerScholars WeAreTeachers 2021-02-24 17:24:23 "@WeAreTeachers @ki… 💡Foun…
10 InnerScholars CommonSenseEd 2021-02-26 17:22:46 "@CommonSenseEd Eve… 💡Foun…
# … with 1,158 more rows, and abbreviated variable name ¹description# A tibble: 688 × 5
sender target created_at text descr…¹
<chr> <chr> <dttm> <chr> <chr>
1 InnerScholars WeAreTeachers 2021-02-27 17:54:35 "@WeAreTeachers Ano… 💡Foun…
2 InnerScholars Bored_Teachers 2021-02-27 17:54:03 "@Bored_Teachers An… 💡Foun…
3 InnerScholars CommonSenseEd 2021-02-27 17:53:42 "@CommonSenseEd Ano… 💡Foun…
4 InnerScholars CommonSenseEd 2021-02-27 17:53:32 "@CommonSenseEd Ano… 💡Foun…
5 InnerScholars CommonSenseEd 2021-02-24 17:25:29 "@CommonSenseEd Eve… 💡Foun…
6 InnerScholars Bored_Teachers 2021-02-24 17:24:50 "@Bored_Teachers Ev… 💡Foun…
7 InnerScholars WeAreTeachers 2021-02-24 17:24:23 "@WeAreTeachers @ki… 💡Foun…
8 InnerScholars CommonSenseEd 2021-02-26 17:22:46 "@CommonSenseEd Eve… 💡Foun…
9 InnerScholars CommonSenseEd 2021-02-26 17:22:37 "@CommonSenseEd @Be… 💡Foun…
10 InnerScholars Bored_Teachers 2021-02-26 17:13:43 "@Bored_Teachers Ev… 💡Foun…
# … with 678 more rows, and abbreviated variable name ¹description# A tibble: 2,336 × 2
name value
<chr> <chr>
1 sender InnerScholars
2 target WeAreTeachers
3 sender InnerScholars
4 target Bored_Teachers
5 sender InnerScholars
6 target CommonSenseEd
7 sender InnerScholars
8 target CommonSenseEd
9 sender InnerScholars
10 target <NA>
# … with 2,326 more rows# A tibble: 1,959 × 2
name value
<chr> <chr>
1 sender InnerScholars
2 target WeAreTeachers
3 sender InnerScholars
4 target Bored_Teachers
5 sender InnerScholars
6 target CommonSenseEd
7 sender InnerScholars
8 target CommonSenseEd
9 sender InnerScholars
10 sender InnerScholars
# … with 1,949 more rowsAnalyze Data
# A tbl_graph: 1541 nodes and 1168 edges
#
# A directed multigraph with 548 components
#
# Node Data: 1,541 × 1 (active)
actors
<chr>
1 InnerScholars
2 WeAreTeachers
3 Bored_Teachers
4 CommonSenseEd
5 <NA>
6 TpT_Official
# … with 1,535 more rows
#
# Edge Data: 1,168 × 5
from to created_at text description
<int> <int> <dttm> <chr> <chr>
1 1 2 2021-02-27 17:54:35 "@WeAreTeachers Anoth… 💡Founder / CEO of Inn…
2 1 3 2021-02-27 17:54:03 "@Bored_Teachers Anot… 💡Founder / CEO of Inn…
3 1 4 2021-02-27 17:53:42 "@CommonSenseEd Anoth… 💡Founder / CEO of Inn…
# … with 1,165 more rowsWarning in cliques(ccss_network, min = 2): At core/cliques/cliquer_wrapper.c:57
: Edge directions are ignored for clique calculations.
Create Sociogram
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#Sentiment Analysis
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 134 rows containing non-finite values (`stat_bin()`).
##Narrative Question: What worldviews dominate the Common Core discourse, based on sentiment analysis of bios of the major actors in the network?
Some conclusions I drew were that neutral unique tweets were far more common than other kinds of unique tweets, but that measure does not tell us what the most dominant actors’ sentiments were. This means having to measure non-unique tweets, since there were many actors outside of the central component, and very few actors dominating that central component. Still, there were vastly more neutral tweets than other types of tweets. This suggests that the dominant actor is seeking to influence others towards an expertise-oriented, neutral sentiment. That actor is InnerScholars.
The audience might use this information to analyze worldviews of users in how they see themselves. I chose to use bios of the users for the sentiment analysis as a way to analyze the attitudes they bring to the platform, through narratives about themselves. Do they want to use the platform to crusade for their point of view, believing the modern world to be against them? Do they instead see themselves as educators for everyone to draw their own conclusions? In future, I would revisit this analysis by tracing the sentiment analyses of the major users through time, using the Twitter time stamps, to see how InnerActor actually has influenced others. I would also draw more explicit visual links between sentiment and major actors.