YouTube comments analysis on race-conscious admission

0. INTRODUCTION

Race-conscious admission has been one of the ways to implement affirmative action in higher education. It has provided certain advantages in admission to applicants who are racially minoritized, such as Black and Hispanic individuals. Numerous controversies surrounding this system have continually arisen, along with a multitude of lawsuits. Since at least 1979, the year of the first landmark ruling from the Supreme Court allowing race to be considered in higher education admission, this system has long been in place in post-secondary education, until recently. However, on June 29th of last year, the Supreme Court overturned previous rulings and issued another landmark ruling stating race itself cannot be used in admission anymore.

This recent ruling is more complicated than previous cases because Asian-American students filed a lawsuit against Harvard University to overturn race-conscious admission, arguing that they have been consistently discriminated against in admissions as this system favored Black and other race-ethnicity students whose academic abilities are lower than Asians.

From the perspective of equity and equality, the public holds a variety of thoughts and opinions. The purpose of this paper is to analyze the major sentiments and topics regarding the race-conscious admission system and the recent ruling, focusing on changes before and after last year’s ruling.

Research questions for this study are as follows: 1. What sentiments are expressed in public comments regarding race-conscious admission? a) Are there differences between sentiments before and after the recent Supreme Court ruling? 2. What topics emerge from public comments? a) Are there differences in topics before and after the recent ruling?

To address these questions, sentiment analysis and topic modeling methods are employed. The primary audience for this study includes policymakers and administrators in higher education institutions, who may use the findings to operate future admission systems.

1. Data preparation

To gain insights into public sentiments and topics, this paper analyzed YouTube comments on broadcasts from five news agencies: Fox News, CNN, NBC, CBS, and ABC. These agencies were chosen to encompass a range of political perspectives, including both Republicans and Democrats, as well as moderates. According to the Pew Research Center (Jurkowitz et al., 2020), Republicans primarily rely on Fox news, while Democrats tend to turn to CNN and NBC. CBS and ABC are agencies that both Republicans and Democrats rely on (Jurkowitz et al., 2020). Republicans and Democrats generally hold different positions on affirmative action, so including both groups contributes to the credibility of the analysis.

To examine the change in the public’s sentiment and topics regarding race-conscious admission before and after the Court ruling, comments were collected from YouTube broadcasts of each news agency. YouTube videos from each news agencies were searched using several key terms such as ‘race-conscious admission’, ‘affirmative action in admission’. The criteria date was set as June 29th 2023, the date of the Supreme Court ruling. comments after the ruling were gathered from broadcasts on and after this date. Consequently, Public comments on the ruling were compiled as well. To collect comments before the ruling, the same key words were used to search for videos, enabling the collection of comments dating back to 2012.

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2. Wrangle

3. Analyze

3a. Word Cloud

3a-1. Word cloud before the ruling

Now we are going to see what words and to what extent they appear in public comments before the Supreme Court ruling.

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It seems that the terms related to race are predominantly mentioned in the comments. Even prior to the broadcasts discussing the ruling, the lawsuit filed by Asian students against Harvard was a major issue related to this topic in the news. I presume that is the reason why “Asian” and “Harvard” appear in this word cloud even before the ruling.

3a-2. Word cloud after the ruling

Now let’s see what words and to what extent they appear in public comments after the Supreme Court ruling.

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Unlike before the ruling, the most frequent words are “people,” “white,” and “student.” Additionally, we can observe that words such as “Asian,” “black,” “race,” “color,” and “college” are frequently mentioned in the comments.

3b. Sentiment analysis

Sentiment analysis identifies whether text expresses positive or negative emotions using lexicons. In this analysis, four lexicons (i.e., afinn, bing, NRC, loughran) are employed, as each lexicon has different list of words.

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ℹ 2,467 more rows

## # A tibble: 6,786 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faces     negative 
##  2 abnormal    negative 
##  3 abolish     negative 
##  4 abominable  negative 
##  5 abominably  negative 
##  6 abominate   negative 
##  7 abomination negative 
##  8 abort       negative 
##  9 aborted     negative 
## 10 aborts      negative 
## # ℹ 6,776 more rows

## # A tibble: 13,872 × 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # ℹ 13,862 more rows

## # A tibble: 4,150 × 2
##    word         sentiment
##    <chr>        <chr>    
##  1 abandon      negative 
##  2 abandoned    negative 
##  3 abandoning   negative 
##  4 abandonment  negative 
##  5 abandonments negative 
##  6 abandons     negative 
##  7 abdicated    negative 
##  8 abdicates    negative 
##  9 abdicating   negative 
## 10 abdication   negative 
## # ℹ 4,140 more rows

3b-1. Sentiment analysis before the ruling

## # A tibble: 11,333 × 5
##    standards name                    time_published          word      value
##    <chr>     <chr>                   <chr>                   <chr>     <dbl>
##  1 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA free          1
##  2 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA motivated     2
##  3 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA desire        1
##  4 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA resolving     2
##  5 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA win           4
##  6 FOX       @akeejackson1474NA      2019-07-11T19:22:33Z NA win           4
##  7 FOX       @hudsonhawk90NA         2018-11-04T21:36:00Z NA unjust       -2
##  8 FOX       @hudsonhawk90NA         2018-11-04T21:36:00Z NA racists      -3
##  9 FOX       @terrybruce5923NA       2018-09-05T20:16:26Z NA damage       -3
## 10 FOX       @dendrobialbright7616NA 2018-07-13T06:47:46Z NA free          1
## # ℹ 11,323 more rows

## # A tibble: 12,166 × 5
##    standards name                time_published          word        sentiment
##    <chr>     <chr>               <chr>                   <chr>       <chr>    
##  1 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA free        positive 
##  2 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA proven      positive 
##  3 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA motivated   positive 
##  4 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA diversified positive 
##  5 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  6 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  7 FOX       @JamesSmith-vk2kyNA 2018-11-06T00:01:22Z NA succeed     positive 
##  8 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA correct     positive 
##  9 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA unjust      negative 
## 10 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA imbalance   negative 
## # ℹ 12,156 more rows

## # A tibble: 12,166 × 5
##    standards name                time_published          word        sentiment
##    <chr>     <chr>               <chr>                   <chr>       <chr>    
##  1 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA free        positive 
##  2 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA proven      positive 
##  3 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA motivated   positive 
##  4 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA diversified positive 
##  5 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  6 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  7 FOX       @JamesSmith-vk2kyNA 2018-11-06T00:01:22Z NA succeed     positive 
##  8 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA correct     positive 
##  9 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA unjust      negative 
## 10 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA imbalance   negative 
## # ℹ 12,156 more rows

## # A tibble: 12,166 × 5
##    standards name                time_published          word        sentiment
##    <chr>     <chr>               <chr>                   <chr>       <chr>    
##  1 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA free        positive 
##  2 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA proven      positive 
##  3 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA motivated   positive 
##  4 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA diversified positive 
##  5 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  6 FOX       @akeejackson1474NA  2019-07-11T19:22:33Z NA win         positive 
##  7 FOX       @JamesSmith-vk2kyNA 2018-11-06T00:01:22Z NA succeed     positive 
##  8 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA correct     positive 
##  9 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA unjust      negative 
## 10 FOX       @hudsonhawk90NA     2018-11-04T21:36:00Z NA imbalance   negative 
## # ℹ 12,156 more rows

## # A tibble: 5 × 5
## # Groups:   standards [5]
##   lexicon standards negative positive ratio
##   <chr>   <chr>        <int>    <int> <dbl>
## 1 AFINN   ABC            335      213 0.636
## 2 AFINN   CBS            186      118 0.634
## 3 AFINN   CNN           4599     3584 0.779
## 4 AFINN   FOX            611      345 0.565
## 5 AFINN   NBC            772      570 0.738

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon standards negative positive sentiment ratio
##   <chr>   <chr>        <int>    <int>     <int> <dbl>
## 1 bing    ABC            363      196      -167 0.540
## 2 bing    CBS            207      143       -64 0.691
## 3 bing    CNN           5227     3400     -1827 0.650
## 4 bing    FOX            756      406      -350 0.537
## 5 bing    NBC            890      578      -312 0.649

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon standards negative positive sentiment ratio
##   <chr>   <chr>        <int>    <int>     <int> <dbl>
## 1 nrc     ABC            363      196      -167 0.540
## 2 nrc     CBS            207      143       -64 0.691
## 3 nrc     CNN           5227     3400     -1827 0.650
## 4 nrc     FOX            756      406      -350 0.537
## 5 nrc     NBC            890      578      -312 0.649

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon  standards negative positive sentiment ratio
##   <chr>    <chr>        <int>    <int>     <int> <dbl>
## 1 loughran ABC            363      196      -167 0.540
## 2 loughran CBS            207      143       -64 0.691
## 3 loughran CNN           5227     3400     -1827 0.650
## 4 loughran FOX            756      406      -350 0.537
## 5 loughran NBC            890      578      -312 0.649

lexicon	standards	negative	positive	ratio	sentiment
AFINN	ABC	335	213	0.6358209	NA
AFINN	CBS	186	118	0.6344086	NA
AFINN	CNN	4599	3584	0.7792998	NA
AFINN	FOX	611	345	0.5646481	NA
AFINN	NBC	772	570	0.7383420	NA
bing	ABC	363	196	0.5399449	-167
bing	CBS	207	143	0.6908213	-64
bing	CNN	5227	3400	0.6504687	-1827
bing	FOX	756	406	0.5370370	-350
bing	NBC	890	578	0.6494382	-312
nrc	ABC	363	196	0.5399449	-167
nrc	CBS	207	143	0.6908213	-64
nrc	CNN	5227	3400	0.6504687	-1827
nrc	FOX	756	406	0.5370370	-350
nrc	NBC	890	578	0.6494382	-312
loughran	ABC	363	196	0.5399449	-167
loughran	CBS	207	143	0.6908213	-64
loughran	CNN	5227	3400	0.6504687	-1827
loughran	FOX	756	406	0.5370370	-350
loughran	NBC	890	578	0.6494382	-312

All ratios are under 1, indicating that comments from all news agencies have more negative words than positive words, regardless of lexicons. With the exception of Afinn, the ratios from the other lexicons are consistent across all news agencies.

Let me illustrate whether there is difference across news agency.

## Warning: Removed 15 rows containing missing values (`geom_bar()`).

Though it would be possibly influenced by the amount of comments, it seems that public comments from Fox News have relatively more negative words than comments from other news agencies. We should note that Republicans mostly rely on Fox News. In their conservative perspective, they might oppose race-conscious admission system; therefore, it makes sense that more negative sentiment appeared in comments of broadcasts from the Fox News.

Let’s look into what positive and negative words are in Fox News comments before the ruling. We are going to make treemap.

It is strange that “Trump” is considered as a positive word in this analysis. However, we can observe that “racist” and “racism” are the most frequently mentioned negative sentiments, and “merit” and “fair” are also frequently discussed in public sentiments on Fox News before the ruling.

Not only for Fox News comments, but also for overall proportions of positive and negative words,we are going to examine proportion of positive and negative sentiments across all news agencies. We are going to use “bing” lexicon as all lexicon shows the same ratios except for Afinn.

We can observe that there are more negative sentiments expressed across all news agencies compared to positive sentiments.

3b-2. Sentiment analysis after the ruling

Now we are going to analyze public sentiment after the Supreme Court ruling, using four lexicons.

## # A tibble: 7,833 × 5
##    standards name             time_published          word        value
##    <chr>     <chr>            <chr>                   <chr>       <dbl>
##  1 FOX       @umpire29NA      2023-07-04T17:07:48Z NA cut            -1
##  2 FOX       @umpire29NA      2023-07-04T17:07:48Z NA yeah            1
##  3 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA dream           1
##  4 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA dream           1
##  5 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA responsible     2
##  6 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA responsible     2
##  7 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA damned         -4
##  8 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA responsible     2
##  9 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA pay            -1
## 10 FOX       @badwabbit1973NA 2023-07-04T15:03:37Z NA war            -2
## # ℹ 7,823 more rows

## # A tibble: 8,825 × 5
##    standards name                time_published          word       sentiment
##    <chr>     <chr>               <chr>                   <chr>      <chr>    
##  1 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA supreme    positive 
##  2 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA corruption negative 
##  3 FOX       @David-pf5tfNA      2023-07-09T01:36:10Z NA merit      positive 
##  4 FOX       @badwabbit1973NA    2023-07-04T15:03:37Z NA damned     negative 
##  5 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA supreme    positive 
##  6 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA capable    positive 
##  7 FOX       @Punisher1830NA     2023-07-03T20:05:47Z NA racism     negative 
##  8 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA loopholes  negative 
##  9 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA rich       positive 
## 10 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA wealthy    positive 
## # ℹ 8,815 more rows

## # A tibble: 8,825 × 5
##    standards name                time_published          word       sentiment
##    <chr>     <chr>               <chr>                   <chr>      <chr>    
##  1 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA supreme    positive 
##  2 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA corruption negative 
##  3 FOX       @David-pf5tfNA      2023-07-09T01:36:10Z NA merit      positive 
##  4 FOX       @badwabbit1973NA    2023-07-04T15:03:37Z NA damned     negative 
##  5 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA supreme    positive 
##  6 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA capable    positive 
##  7 FOX       @Punisher1830NA     2023-07-03T20:05:47Z NA racism     negative 
##  8 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA loopholes  negative 
##  9 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA rich       positive 
## 10 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA wealthy    positive 
## # ℹ 8,815 more rows

## # A tibble: 8,825 × 5
##    standards name                time_published          word       sentiment
##    <chr>     <chr>               <chr>                   <chr>      <chr>    
##  1 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA supreme    positive 
##  2 FOX       @andypearson3776NA  2023-08-30T03:50:45Z NA corruption negative 
##  3 FOX       @David-pf5tfNA      2023-07-09T01:36:10Z NA merit      positive 
##  4 FOX       @badwabbit1973NA    2023-07-04T15:03:37Z NA damned     negative 
##  5 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA supreme    positive 
##  6 FOX       @darzellenone7626NA 2023-07-04T14:03:29Z NA capable    positive 
##  7 FOX       @Punisher1830NA     2023-07-03T20:05:47Z NA racism     negative 
##  8 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA loopholes  negative 
##  9 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA rich       positive 
## 10 FOX       @chuck9380NA        2023-07-03T02:10:42Z NA wealthy    positive 
## # ℹ 8,815 more rows

## # A tibble: 5 × 5
## # Groups:   standards [5]
##   lexicon standards negative positive ratio
##   <chr>   <chr>        <int>    <int> <dbl>
## 1 AFINN   ABC            245      182 0.743
## 2 AFINN   CBS           2233     1863 0.834
## 3 AFINN   CNN            324      230 0.710
## 4 AFINN   FOX            815      646 0.793
## 5 AFINN   NBC            725      570 0.786

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon standards negative positive sentiment ratio
##   <chr>   <chr>        <int>    <int>     <int> <dbl>
## 1 bing    ABC            282      189       -93 0.670
## 2 bing    CBS           2607     1988      -619 0.763
## 3 bing    CNN            380      238      -142 0.626
## 4 bing    FOX           1022      737      -285 0.721
## 5 bing    NBC            825      557      -268 0.675

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon standards negative positive sentiment ratio
##   <chr>   <chr>        <int>    <int>     <int> <dbl>
## 1 nrc     ABC            282      189       -93 0.670
## 2 nrc     CBS           2607     1988      -619 0.763
## 3 nrc     CNN            380      238      -142 0.626
## 4 nrc     FOX           1022      737      -285 0.721
## 5 nrc     NBC            825      557      -268 0.675

## # A tibble: 5 × 6
## # Groups:   standards [5]
##   lexicon  standards negative positive sentiment ratio
##   <chr>    <chr>        <int>    <int>     <int> <dbl>
## 1 loughran ABC            282      189       -93 0.670
## 2 loughran CBS           2607     1988      -619 0.763
## 3 loughran CNN            380      238      -142 0.626
## 4 loughran FOX           1022      737      -285 0.721
## 5 loughran NBC            825      557      -268 0.675

lexicon	standards	negative	positive	ratio	sentiment
AFINN	ABC	245	182	0.7428571	NA
AFINN	CBS	2233	1863	0.8343036	NA
AFINN	CNN	324	230	0.7098765	NA
AFINN	FOX	815	646	0.7926380	NA
AFINN	NBC	725	570	0.7862069	NA
bing	ABC	282	189	0.6702128	-93
bing	CBS	2607	1988	0.7625623	-619
bing	CNN	380	238	0.6263158	-142
bing	FOX	1022	737	0.7211350	-285
bing	NBC	825	557	0.6751515	-268
nrc	ABC	282	189	0.6702128	-93
nrc	CBS	2607	1988	0.7625623	-619
nrc	CNN	380	238	0.6263158	-142
nrc	FOX	1022	737	0.7211350	-285
nrc	NBC	825	557	0.6751515	-268
loughran	ABC	282	189	0.6702128	-93
loughran	CBS	2607	1988	0.7625623	-619
loughran	CNN	380	238	0.6263158	-142
loughran	FOX	1022	737	0.7211350	-285
loughran	NBC	825	557	0.6751515	-268

Just like the sentiments and ratios before the Court ruling, all ratios are under 1, indicating that comments from all news agencies have more negative words than positive words, regardless of lexicons. Additionally, the sentiment ratios in each news agency are identical across lexicons except for Afinn.

I created bar graph of ratio of each news agency to examine difference among them.

## Warning: Removed 15 rows containing missing values (`geom_bar()`).

The sentiment ratios appear similar across news agencies, but comments on CNN have relatively more negative words compared to positive words. Considering CNN is a news agency that Democrats rely on, they might usually support race-conscious admission. Then it makes sense that there are more negative sentiments regarding the Supreme Court ruling, which bans the consideration of race itself in future admission systems.

Let me further examine the sentiments of comments from CNN after the Court ruling.

Similarly with comments of Fox News of before the Court ruling, “racism”, “racist”, and “discrimination” were mentioned most frequently in negative sentiments.

Not only for CNN comments, but also for overall proportions of positive and negative words, we are going to examine proportion of positive and negative sentiments across all news agencies, using sentiment examined by bing lexicon. (As I mentioned before, all lexicon shows the same ratios.)

Compared to the proportion of before the Court ruling, there are slightly more positive sentiments in comments after the Court ruling. This might mean that more people shows little more agreement with banning affirmative action in higher education. However, there are more negative sentiments than positive one toward race-conscious admission even after the ruling.

Let me show this difference with sentiment proportion of before and after the ruling at the same time.

As we can see, there is a slightly higher proportion of positive sentiment after the ruling than before the ruling. However, it is not that big change. Additionally, we should also note that there are more negative sentiments in both before and after the ruling.

3c. Topic Modeling

Topic modeling is part of a class of text analysis methods that analyze “bags” or groups of words together—instead of counting them individually in order to capture how the meaning of words is dependent upon the broader context in which they are used in natural language (Bail, 2020). Two main ways of topic modeling are Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM). This analysis employs both LDA and STM will be employed to find topics within public comments.

Similarly with sentiment analysis conduct, We are going to see whether there is difference in topics between comments from before the ruling and comments from after the ruling.

3c-1. Topic modeling before the ruling

We are going to first examine topics in comments of before the ruling.

We have to set the right number of K to conduct topic modeling. There are several measures to find the K.

## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the ldatuning package.
##   Please report the issue at <https://github.com/nikita-moor/ldatuning/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Graph shows that common crossover point is nearly 4. So we are going to use 4 for the value of k.

Latent Dirichlet Allocation (LDA) modeling for before the ruling

An LDA model was run using 4 as topic count K. And the data excluded the term “affirmative” and “action” because of its prevalence. As affirmative action is a topic itself, excluding these terms would be reasonable.

## A LDA_VEM topic model with 4 topics.

Let’s look at longer lists of the terms in each topic to see more differentiation.

##       Topic 1  Topic 2   Topic 3    Topic 4   
##  [1,] "peopl"  "harvard" "asian"    "school"  
##  [2,] "racist" "admiss"  "white"    "student" 
##  [3,] "white"  "base"    "black"    "asian"   
##  [4,] "racism" "race"    "american" "race"    
##  [5,] "color"  "student" "peopl"    "colleg"  
##  [6,] "skin"   "don’t"  "african"  "peopl"   
##  [7,] "trump"  "it’"    "countri"  "black"   
##  [8,] "minor"  "asian"   "latino"   "score"   
##  [9,] "equal"  "merit"   "privileg" "educ"    
## [10,] "don"    "monei"   "america"  "american"

The four topics look slightly different each other.

Now we are going to further examine these topics.

## # A tibble: 29,324 × 3
##    topic term       beta
##    <int> <chr>     <dbl>
##  1     1 cnn    3.12e- 3
##  2     2 cnn    1.71e- 6
##  3     3 cnn    2.37e-11
##  4     4 cnn    6.54e-13
##  5     1 domest 2.14e- 4
##  6     2 domest 1.63e-13
##  7     3 domest 1.15e- 5
##  8     4 domest 3.04e-20
##  9     1 lt     6.24e- 4
## 10     2 lt     3.13e-22
## # ℹ 29,314 more rows

Considering graph each topic could be:

Topic 1: Trump administration policy which does not consider skin color anymore - could it be racism? Topic 2: Whether Harvard University’s race-based admission does not consider Asians Topic 3: How to consider various race-ethnicity in higher education Topic 4: Issues in schools with race-conscious admission (especially regarding score discrepancy among students)

Now we are going to see relative popularity of each topic, measured by the statistic gamma.

## # A tibble: 29,324 × 3
##    topic term       beta
##    <int> <chr>     <dbl>
##  1     1 cnn    3.12e- 3
##  2     2 cnn    1.71e- 6
##  3     3 cnn    2.37e-11
##  4     4 cnn    6.54e-13
##  5     1 domest 2.14e- 4
##  6     2 domest 1.63e-13
##  7     3 domest 1.15e- 5
##  8     4 domest 3.04e-20
##  9     1 lt     6.24e- 4
## 10     2 lt     3.13e-22
## # ℹ 29,314 more rows

## # A tibble: 12,644 × 3
##    document         topic gamma
##    <chr>            <int> <dbl>
##  1 @0Diazzz0NA          1 0.793
##  2 @1115keyNA           1 0.561
##  3 @111GtfoNA           1 0.435
##  4 @1231nlpNA           1 0.164
##  5 @13HangfireNA        1 0.655
##  6 @18890426NA          1 0.118
##  7 @1940limitedNA       1 0.349
##  8 @1966bluemaxNA       1 0.140
##  9 @1titansNA           1 0.102
## 10 @236HerbinatorNA     1 0.659
## # ℹ 12,634 more rows

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c(terms)`.

Topic	Expected topic proportion	Top 10 terms
Topic 1	0.320	peopl, racist, white, racism, color, skin, trump, minor, equal, don
Topic 4	0.252	school, student, asian, race, colleg, peopl, black, score, educ, american
Topic 3	0.218	asian, white, black, american, peopl, african, countri, latino, privileg, america
Topic 2	0.209	harvard, admiss, base, race, student, don’t, it’, asian, merit, monei

The above chart tells that the topics appear in proportions, varying between 20.9% and 32%. We should expect topic 1 appears 32% in comments, topic 4 appears 25.2%, topic 3 appears 21.8%, topic 2 appears 20.9%.

Structural Topic Modeling (STM) for before the ruling

There are many ways to conduct topic modeling besides LDA. Structural Topic Modeling (STM) is one of the popular techniques in recent year and very similar to LDA (Bail, 2020). STM uses meta data about documents to enhance the allocation of words to latent topics within a corpus (Bail, 2020).

## Building corpus... 
## Converting to Lower Case... 
## Removing punctuation... 
## Removing stopwords... 
## Removing numbers... 
## Stemming... 
## Creating Output...

## A topic model with 4 topics, 5094 documents and a 8967 word dictionary.

Now, we are going to see to what extent topics are distributed.

## 필요한 네임스페이스를 로딩합니다: servr

Although topic 3 and 4 are partly overlapped, other topics are separated each other.

STM with four topics shows the following chart that includes expected topic proportions and top words in each topic.

Approximately 38% of comments are topic 2: Asian-Americans’ argument that college admission has yielded discrimination, 33% of comments are topic 3: Whether Harvard University can make justice for both Whites and Blacks, with affirmative action, 19% of comments are topic 4: Public opinions on race and job opportunity, 16% of comments are topic 1: Actual benefit that students in minor race-ethnicity receive in university.

Reading tea leaves

We can see examples of comments in each topic as follows, though it includes foreign language other than English.

## 
##  Topic 1: 
##       NA @WE WUZ VIKANGS!!! n shiet. "outrage" me.  friggin lil punk...lol... 
##  Topic 2: 
##       NA ​@@cj2708 I had a much longer, more comprehensive reply to the last message, however I decided to save it in a word doc as a personal journal entry and respond with a shorter statement because the reply was becoming too long and the argument one that I don&#39;t want to expand upon. I like to have these discussions, however when I have to explain every little thing in detail the whole discussion becomes less appealing. The part where the discussion becomes somewhat annoying is when you claim that Asians are as individualistic as Americans, which is far from the truth. It&#39;s clear that you were offended. <br><br><br>I never stated that &quot;if you belong to a certain Ethnicity, you MUST act a certain way&quot;. Don&#39;t misrepresent what I said. We are not talking about individuals, we are talking about groups. When analyzing groups, generalizations are made to most accurately depict the group in a concise manner. This does not assert in absolutes, but instead in tendencies over time periods. In reality different groups of people behave differently.  An example is the following statement: Christians are more tolerant of gay people when compared to Muslims. Analysis supports the claim very easily because it is measurable when looking at how certain nations persecute these individuals differently. There are, nonetheless, exceptions to this &#39;rule&#39; or generalization. What we are talking about are tendencies of certain groups. Tendencies which reflect the values and ideals of the groups in question. You are implying that if there are exceptions then generalizations cannot be made and, therefore, you are also inadvertently implying that one cannot say anything about any group of people because exceptions exist in every group (within a reasonable sample size of course). The whole basis of this discussion is analyzing the scores of Asians, which tend to be higher based on X, Y and Z, which you have already stated. The contradiction is that you argue against these very generalizations which show up statistically. These very statistics support the claim that there are different tendencies within the various groups. <br><br><br>I also find it somewhat hard to believe that you legitimately have lived in these separate areas, as you don&#39;t actually understand the difference in thought to any significant degree. It is possible that you haven&#39;t explored the various populations you&#39;ve been around pertaining to certain aspects of life in dialogue. Nonetheless, there is a distinct rift in thought along the Collectivism vs. Individualism spectrum; both of which have good arguments for and against. If you want to understand why Asians think how they do, you have to look at their society&#39;s history. I&#39;m assuming you are Asian American, but not actually very  American ethnically given your interpretation of our people. <br><br><br>On Chinese(Asian) vs American culture:<br>The dominant player in the discussion would be China, which boasts a large immigrant population in the US and is the dominant group of people in what people consider Asia (excluding India, The Middle East and Russia due to geological, ethnic and genetic variation) <br>Chinese culture, to a large degree, was shaped by Confucius more than 2 thousand years ago. Although there was more diversity in thought, the Chinese emperors decided that Confucianism was the best route and indoctrinated the rest of the population to reflect this decision. Confucianism was likely favored and, therefore, adopted because of the behavior it promoted among the general population. A major aspect of the teaching goes like this: the father is the head of the household and the rest of the family should be obedient, The emperor is to the state what the father is to the household and those under the emperor should be obedient to the emperor like a wife and kids are to the father. By relating the family to society, for a man to revolt against the emperor would be similar to condoning his own wife revolting against himself and, therefore, undermine his own stature in the home. This level of obedience is argued to promote order and stability in the nation, which it did for much of Chinese history and is still prevalent in China today despite the changes occurring. It is very intelligent in many ways, however it is very Collectivist in nature. <br>The United States is on the other end of the spectrum, as it was born of revolution and rebellion and still embodies many of those ideals today; such as life, liberty and the pursuit of happiness all of which trump any justification for restriction of individual activity and freedom. The right to free speech, The right to bear arms, the right to assemble, the right to property, the right to privacy, the right to &quot;no taxation without representation&quot;. There are pros and cons to both systems. China vs. America, Collectivist vs Individualist. Not necessarily better or worse in either direction, just different.<br><br><br>I also find it somewhat offensive to describe Americans as &quot;gun-slinging red-necking&quot; simply for valuing the empowerment of the general population and the individual via being armed: an idea which is a fundamental aspect of American culture and is one of the most effective checks on the power of the central government. 
##  Topic 3: 
##       Right-Wing: Fake News by Fake Experts, Left-Wing: Deplorables and Uneducated! This is the outcome of Harvard Fake Graduates because of the intervention of Affirmative Action, the Deplorables will surely qualify Harvard anytime because they can identify Fake Experts and Fake News. hi hi hi hi! I can&#39;t think seriously... the world turns funnier and funnier because of left-wing and right-wing politics. NA 
##  Topic 4: 
##       &quot;We relate to you such accounts of earlier towns : some of them are still standing, while others have cease to exist,<br>We did not wrong them, they wronged themselves.. (100,101 Hud)<br><br>In the ancient chronicles events relating to King and military generals have been mentioned but events relating to prophets and their communities find no place therein.<br>History as written by man has omitted those very things which in the eyes of the Creator, were most worthy of mention.<br>However the Quran describes at great length the dawah work performed by the Prophets and the reaction of their addressees to the call. Other matters have been ignored by the Quran as being of no importance.<br><br>Whenever a man indulges in arrogance and transgression, he does so on the strength of some notion.<br>He considers something or beings as supports and thinks that they will continue to be so in difficult times.<br>But these supports will be such for only so long as the God gives them leave.<br>When according to the law of God, the period of respite ends and God announces His final verdict, then man will come to know that the assumptions which in his foolishness he had made about his &quot;supports&quot; were all false.<br><br>The QURAN, translation and commentary by Maulana Wahiduddin Khan free download from <a href="http://www.cpsglobal.org/">www.cpsglobal.org</a> <br>دعوت کا اسلوب<br><br>ربیعہ بن عباد الدیلی روایت کرتے ہیں کہ انهوں نے رسول اللہ کو ذوالمجاز کے سوق میں دیکها- آپ لوگوں کے پاس جاتے اور ان سے کہتے : یاایهاالناس قولوا :لا اله الااللہ ، تفلحوا(مسند احمد : 16063)- اس روایت کو لے کر بعض لوگ کہتے ہیں کہ دعوت کا صحیح طریقہ یہ ہے کہ لوگوں سے مل کر ان کو کلمہ پڑهنے کے لیے کہا جائے- اس کے علاوه دعوت کا کوئی اور اسلوب درست نہیں هو سکتا- <br><br>اگر دعوت کا یہی واحد اسلوب تها تو قرآن میں یقینا اس کو بتایا گیا هوتا- مگر هم دیکهتے ہیں کہ قرآن میں ایسی کوئی هدایت موجود نہیں- البتہ قرآن کے مطالعے سے اس سلسلے میں کئی اسلوب ملتے ہیں- مثلا  یاایها المدثر قم فآنذر (74:1-2)- اسی طرح اس سلسلے میں قرآن کی ایک آیت یہ ہے : یا ایها الرسول بلغ ما انزل الیک من ربک (5:66)- خود احادیث سے بهی دوسرے اسالیب معلوم هوتے ہیں- مثلا ایک روایت میں یہ الفاظ آئے ہیں : عرض علیهم الاسلام ، وتلا علیهم القرآن (سیت ابن هشام :1/428) ، وغیره -<br><br> اصولی طور پر دعوت کا صرف ایک اسلوب ہے، اور وه قرآن کی ایک آیت میں اس طرح بتایا گیا ہے : قل لهم فی انفسهم قولا بلیغا (4:63)- یعنی ان سے ایسے اسلوب میں کلام کرو جو ان کے ذہن کو ایڈریس کرنے والا هو- اس قرآنی ہدایت کے مطابق ظاہری اعتبار سے دعوت کے مختلف اسلوب هو سکتے ہیں- اسلوب ہمیشہ مخاطب کے اعتبار سے متعین هو گا- پہلے مخاطب کا مطالعہ کیا جائے گا، اور پهر اس کے ذہن کے اعتبار سے ایسا اسلوب اختیار کیا جائے گا جو اس کے ذہن کو اپیل کرنے والا هو-<br><br>دعوت کا نشانہ یہ ہے کہ مدعو کے اوپر اتمام حجت هو جائے : یعنی تاکہ رسولوں کے بعد لوگوں کے پاس اللہ کے مقابلہ میں کوئی حجت باقی نہ رہے- (<a href="https://www.youtube.com/watch?v=19i_MfsIDg0&amp;t=256">4:16</a>5)  حجت کبهی کسی قسم کے مقرر الفاظ کی ادائیگی سے نہیں هوتی- حجت کے لیے ضروری ہے کہ اس میں مدعو کے ذہن کی رعایت کی گئی هو- حقیقت یہ ہے کہ دعوت کا اسلوب مدعو کے اعتبار سے متعین هو گا، نہ کہ کسی اور اعتبار سے-<br><br>الرسالہ، ستمبر 2015<br>مولانا وحیدالدین خان NA

3c-2. Topic modeling after the ruling

Now we are going to examine topics in comments of after the ruling.

Findingn K

We have to set the right number of K to conduct topic modeling. There are several measures to find the K, and graph shows that common crossover point is nearly 6.

We are going to use 6 as a k value.

Latent Dirichlet Allocation (LDA) modeling for after the ruling

## A LDA_VEM topic model with 6 topics.

Let’s look at longer lists of the terms in each topic to see more differentiation

##       Topic 1    Topic 2   Topic 3      Topic 4     Topic 5    Topic 6    
##  [1,] "racism"   "student" "white"      "race"      "school"   "court"    
##  [2,] "peopl"    "color"   "peopl"      "colleg"    "student"  "suprem"   
##  [3,] "colleg"   "black"   "black"      "admiss"    "kid"      "decis"    
##  [4,] "racist"   "colleg"  "asian"      "legaci"    "parent"   "justic"   
##  [5,] "govern"   "skin"    "american"   "base"      "colleg"   "rule"     
##  [6,] "countri"  "race"    "minor"      "discrimin" "rich"     "law"      
##  [7,] "democrat" "peopl"   "major"      "equal"     "monei"    "discrimin"
##  [8,] "system"   "base"    "final"      "applic"    "fund"     "god"      
##  [9,] "commun"   "divers"  "republican" "merit"     "educ"     "vote"     
## [10,] "race"     "merit"   "qualifi"    "educ"      "children" "racist"

The six topics looks slightly different each other. Now we are going to further examine these topics.

## # A tibble: 37,908 × 3
##    topic term       beta
##    <int> <chr>     <dbl>
##  1     1 accept 2.26e-19
##  2     2 accept 7.65e- 3
##  3     3 accept 6.02e- 7
##  4     4 accept 3.14e-12
##  5     5 accept 3.55e- 3
##  6     6 accept 6.90e-17
##  7     1 action 3.87e-20
##  8     2 action 7.11e- 4
##  9     3 action 4.79e- 4
## 10     4 action 2.11e- 4
## # ℹ 37,898 more rows

The Graph depicts each topic as follows: Topic 1: Whether banning race-conscious admission could be racism Topic 2: Contemplating students’ skin color versus their merits in admission for fostering diversity in colleges Topic 3: Considering students’ qualification in admission regardless of race or major/minor groups Topic 4: Legacy admission as another form of discrimination system alongside race-conscious admission Topic 5: The advantages of kid with rich parents and highly funded schools resulting from the banning of race-conscious admission Topic 6: Justification of the Supreme Court’s decision; whether it signifies achieving justice or aligns with racist beliefs Now we are going to see the relative popularity.

## # A tibble: 37,908 × 3
##    topic term       beta
##    <int> <chr>     <dbl>
##  1     1 accept 2.26e-19
##  2     2 accept 7.65e- 3
##  3     3 accept 6.02e- 7
##  4     4 accept 3.14e-12
##  5     5 accept 3.55e- 3
##  6     6 accept 6.90e-17
##  7     1 action 3.87e-20
##  8     2 action 7.11e- 4
##  9     3 action 4.79e- 4
## 10     4 action 2.11e- 4
## # ℹ 37,898 more rows

## # A tibble: 19,608 × 3
##    document         topic   gamma
##    <chr>            <int>   <dbl>
##  1 @--xX_Xx--NA         1 0.0403 
##  2 @05jorgeruizNA       1 0.0254 
##  3 @0penthaughtzNA      1 0.0101 
##  4 @10-10-NA            1 0.0354 
##  5 @1001HobbiesNA       1 0.00998
##  6 @1219supermanNA      1 0.0593 
##  7 @12NordseeNA         1 0.0137 
##  8 @1515candoNA         1 0.0223 
##  9 @17NateJohnsonNA     1 0.00897
## 10 @18S12NA             1 0.0179 
## # ℹ 19,598 more rows

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c(terms)`.

Topic	Expected topic proportion_after	Top 10 terms
Topic 2	0.205	student, color, black, colleg, skin, race, peopl, base, divers, merit
Topic 6	0.188	court, suprem, decis, justic, rule, law, discrimin, god, vote, racist
Topic 4	0.181	race, colleg, admiss, legaci, base, discrimin, equal, applic, merit, educ
Topic 3	0.150	white, peopl, black, asian, american, minor, major, final, republican, qualifi
Topic 5	0.146	school, student, kid, parent, colleg, rich, monei, fund, educ, children
Topic 1	0.130	racism, peopl, colleg, racist, govern, countri, democrat, system, commun, race

The above chart tells that the topics appear in proportions, varying between 13% and 20.5%. We should expect that topic 2 appears 20.5% in comments, topic 6 appears 18.8%, topic 4 appears 18.1%, topic 3 appears 15%, topic 5 appears 14.6 %, topic 1 appears 13% in comments.

Structural Topic Modeling (STM) for after the ruling.

## Building corpus... 
## Converting to Lower Case... 
## Removing punctuation... 
## Removing stopwords... 
## Removing numbers... 
## Stemming... 
## Creating Output...

## A text corpus with 5164 documents, and an 8108 word dictionary.

## A topic model with 6 topics, 5164 documents and a 8108 word dictionary.

Now, we are going to see to what extent topics are distributed.

Topic 1, 2, and 3 are overlapped each other, but topic 4, 5, and 6 distributed.

We will assign a label to each topic based on their anticipated proportion in the dataset.

Approximately 23% of comments are topic 4: Whether the Court decision was good or racism regarding equal admission, especially when considering legacy admission issue, 18% of comments are topic 6: Question about the right time for universities to terminate race-conscious admission which considers students’ skin colors, 17% of comments are topic 2: How long does it take for school to change their admission system based on law (i.e., the Supreme Court ruling), 16% of comments are topic 5: Now we should still think about better admission system than race (color)-based admission, 15% of comments are topic 3: The influence of the Supreme Court ruling on merit based admission that many Black applicants should compete with other races, 14% of comments are topic 1: The influence of banning affirmative action on Asian students, kids, and diversity in U.S.

Reading tea leaves

We can see examples of comments in each topic as follows

## 
##  Topic 1: 
##       « أَعُوذُ بِكَلِمَاتِ اللهِ التَّامَّاتِ مِنْ شَرِّ مَا خَلَقَ »<br>(( I seek refuge by the complete, perfect words of Allaah from the evil of what He has created )) NA 
##  Topic 2: 
##       Pronuncio-me como Cavaliere Della Madre; com insígnias para honrar a América e levantar personas para uma Civilização de Valor Pessoal e também Político em países como Austrália, Singapura, etc, etc. Valho-me de conceitos-avantes e valores intrínsecos ao suporte do viver de nossa espécie (e quiçá, outras; segundo o que vemos via JWT).
<br>Defendo que Trump foi &quot;assaltado&quot; em seu cargo na cara dos Americanos e do Mundo; e isto se deu por conluio de pastores evangélicos ladro-narco-milício-corruptos com uma maçonaria estragada, unidos em tráfico e milícias covardes, sob o &quot;bafo&quot; baderneiro-sabotador de países encoberto (em vermelho-preto-azul. numa ruína maquinada da Fé legítima e Valores legítimos solapados) sorrateiramente num AFRO-Nazismo (que persegue, espreita, difama, rouba, sabota, mata, destruindo empresas e a Sociedade, e descaradamente outras etnias; para a soberba e regalo de bandos malignos que posicionaram &quot;bonecos&quot; criminosos em cargos máximos já em várias nações; tendo isto sido encubado no Brasil pela podridão &quot;pulhítica&quot; que ali se vê).
<br>Há príncipes “fajutos” atuando deslavadamente nesse conluio de “empoderamento” da canalhice que está desgraçando as empresas e finanças em várias partes do Mundo; que está destruindo o Brasil (cite-se a destruição insana em Angra dos Reis). Quanto à isso, um aviso:  A Celestial Madre os avisou; e já está na mão dos verdadeiros poderosos a “seta que voa de dia”.
<br>Conquanto, é de Valor, Fibra, e Fé, que não só os Americanos devem lançar à frente o Brio de suas faces e recuperar desses bandidos o que nos roubaram e destruíram.
<br>OBS: Essa desgraça arrebentou a Política e Sociedades países na América do Sul, rumou para os EUA, Austrália, e já atraiçoou a soberania do povo russo. 
<br>Putin deve mirar em incursões de “missões” do submundo “da paz”; embustes gritantes entupiram a América de tráfico-milicianos antes das eleições americanas; e agora os ladro-corruptos “abençoados” intentam desarmar os americanos (como se fez deploravelmente no território brasileiro; onde o povo padece vigiado por um “deus no controle”, que nada mais é que, a mais perversa e depravada corrupção e desfaçatez criminosa, generalizada, e sem escape -- se a atenção dos poderes internacionais não acudir). NA 
##  Topic 3: 
##       NA @@scottowensbyable My grandmother was a book keeper @ Blake, Moffet and Towne Papaer in San Francisco.<br>She was born at the turn of the century and lifed through The San Francisco Earthquake<br>WWI, the Spanish Flu, the Depression, WWII, and all supsequesnt MIC wars up until she died in 96&#39; at 90 and a self-made millionaire.<br>She didn&#39;t go to college. 
##  Topic 4: 
##       NA ​@@hartfordsignpost589And Dr&#39;s, accountants, sales reps, policemen, firemen, etc etc.<br>Enough of these woke racist quotas! 
##  Topic 5: 
##       NA @@goldbud2287 <br>Go Jack Smith, Bragg and Willis in Georgia. Send your losing cult leader all of your money.🤡🤭💰💰💰 
##  Topic 6: 
##       NA ​​​@@wgsmit02Many people go by what the MEDIA pushes and promotes and people tend to forget that which is HATE AND PRIVILEGE.....people use that energy in their everyday lives. Some people are TAUGHT HATE😮💯<br><br>Although you are right about what you said also....it&#39;s deeper than all of what they have people fighting over!

4. Conclusion

Key insights

The purpose of this paper is to see if there is difference in sentiments and topics regarding race-conscious admission among the public before and after the Supreme Court ruling, which banned race-conscious admission. The results show that there are differences in both sentiments and topics between before and after the ruling.

Specifically, in sentiment analysis, a slightly higher proportion of positive sentiment has been observed in comments from broadcasts after the Court ruling compared to the proportion of sentiments in comments before the ruling. However, it is important to note that the proportion of positive and negative sentiments on race-conscious admission tends toward negative sentiments. Thus, overall sentiments toward race-conscious admission are generally more negative than positive.

Additionally, different topics were identified from each set of comments before and after the ruling. Before the Court ruling, public comments on race-conscious admission suggested four different topics as follows, which were identified through a combination of LDA and STM: 1) Trump administration’s policy banning affirmative action 2) Harvard University’s race-based admission policies and discrimination against Asian-Americans 3) How to implement affirmative action in higher education without alleged discrimination
4) The actual benefits that students from minority racial and ethnic backgrounds receive in universities.

After the Court ruling, topics from public comments are as follows; these are also a combination of LDA and STM: 1) Justification of the decision from the Supreme Court; whether banning race-conscious admission could be racism? 2) Considering students’ race versus merits in admission 3) Legacy admission issue 4) How to achieve equity without considering race 5) The appropriate timing to terminate race-conscious admission system 6) How long it takes for schools to incorporate the Supreme Court ruling into their admission system? 7) The impact of banning affirmative action on Asian students, kids, and diversity in U.S.

There is a need to focus on the finding that topics regarding race-conscious admission became much more varied and specific after the Court ruling. The public questioned the justification of ruling and whether it is the right time to revoke affirmative action. Additionally, they are interested in more specific ways to realize race-neutral measures, along with ramification in U.S. society.

Given the Supreme Court ruling would be much more influential on institutions actively implementing affirmative action, it requires actions based on deliberation and reflection of public concerns.

Furthermore, from the perspective that affirmative action is reverse discrimination, the legacy admission system, which refers to the practice of giving preferential treatment to applicants who have family members who attended the same institution, has arisen in controversy. In this analysis, the public also raised questions and concerns about it; if race-conscious admission violates the Constitution due to its inequality, how about legacy admission? Is this fair system? Therefore, policymakers and administrators should prepare how they might answer the above inquiries and handle the admission system to create a fairer academic environment.

Limitation of this analysis

I believe there are no ethical or legal concerns in this analysis, but there is a limitation pertaining to the data analyzed in this paper. While YouTube comments provide opinions and thoughts from the publics, comments are brief and respond to specific news topics. Therefore, they might not include contents as comprehensive or specific as that found in Tweeter or news articles regarding race-conscious admission. In a follow-up study, using another dataset would allow researcher to gain more comprehensive sentiments and topics with greater insights.

Reference

Bail, C. (2020). Topic modeling. Retrieved from https://sicss.io/2020/materials/day3-text-analysis/topic-modeling/rmarkdown/Topic_Modeling.html#running-your-first-topic-model

Jurkowitz, M., Mitchell, A., Shearer, E., & Walker, M. (2020). Americans are divided by party in the resources they turn to for political news. Pew Research Center. Retrieved from https://www.pewresearch.org/journalism/2020/01/24/americans-are-divided-by-party-in-the-sources-they-turn-to-for-political-news/