Pacific B usiness R eview I nternational

A Refereed Monthly International Journal of Management Indexed With Web of Science(ESCI)
ISSN: 0974-438X
Impact factor (SJIF):8.396
RNI No.:RAJENG/2016/70346
Postal Reg. No.: RJ/UD/29-136/2017-2019
Editorial Board

Prof. Mahima Birla
(Editor in Chief)

Dr. Khushbu Agarwal
(Editor)

Dr. Asha Galundia
(Circulation Manager)

Editorial Team

A Refereed Monthly International Journal of Management

Understanding The Police, The Administration and The Governed Through Social Media Analytics

Dr. Archana Shrivastava,

Director &Professor,

Balaji Institute of Modern Management,

Sri Balaji University, Pune

 

Ashish Shrivastava,

Consultant,

Speed Mart Pvt Ltd.

 

Case Synopsis

The case setting is based on the real-life event when George Floyd, an African American, was suspected of having used counterfeit money to buy a pack of cigarettes. The case was reported to the police and three white policemen arrived on the scene. George Floyd was pinned to the ground and Police Officer Derek Chauvin pressed his keen on Floyd’s neck choking him. Floyd begged to be released but to no avail and was suffocated to death.  The event sparked widespread protests across America which soon spread to other parts of the world. The American administration miscalculated the sentiments of the common man and decided to use the army against them. This further increased the divide between the police and the protestors. Tainted tweets on social media by President Donald Trump further escalated the anger of the protestors. This case study collected sixty thousand tweets from the Twitter Social Media platform and qualitatively analyzed them using social media analytics. The case study endeavors to emphasize the need for taking cognizance of public sentiments in management and the use of social media analytics in understanding them. The case study adopts a holistic framework that combines sentiment analysis, emotion detection, spatial/temporal analysis to understand the range of issues surrounding the # Black Lives Matter movement.

Keywords: Black Lives Matter, Sentiment analysis, Social Media analytics, Sentiment Index, Qualitative analysis

 

Introduction

“I Can’t Breathe!”

On May 25, 2020, George Floyd, a 46-year-old black American man, was killed in Minneapolis, Minnesota, during an arrest for allegedly using a counterfeit bill. George Floyd had purchased a pack of cigarettes with what the clerk doubted to be a counterfeit $20 bill. Three police squad cars rushed to confront him as he sat in the driver’s seat of a blue Mercedes SUV. Derek Chauvin, a white police officer, knelt on Floyd's neck for almost eight minutes while Floyd was handcuffed and lying face down, begging for his life and repeatedly saying "I can't breathe”. Officers J. Alexander Kueng and Thomas Lane further restrained Floyd, while Officer Tou Thao prevented bystanders from intervening. During the final two minutes, Floyd lay motionless and had no pulse while Chauvin disregarded onlookers' requests to remove his knee, which he only did when the medics told him to. Floyd’s death triggered the subsequent protests against police brutality, police racism, and lack of police accountability. Unrest began in local protests in the Minneapolis–Saint Paul area before quickly spreading nationwide and across 60 countries showing support for and solidarity with the ‘Black Lives Matter’ campaign. Over 2,000 cities in the US had seen demonstrations as of June 13, 2020. Social media was live with the overwhelming agony, pain and anguish people were expressing across the world.

The incident once again brought into focus concerns over the law enforcement’s discriminatory attitude towards the African American minority.  Initially, the knee-jerk reaction of the Trump Government was harsh and the US President Donald Trump threatened the use of force in dealing with protests against the death of George Floyd. He equated the participants of the movement as “thugs” on Twitter and made the infamous statement- “Any difficulty and we will assume control but, when the looting starts, the shooting starts.

 

The evening of 1st June 2020 sawa line of nine military trucks carrying National Guard troops in helmets and camouflage uniforms make a beeline onto the White House grounds and down a narrow alley near the West Wing. The military trucks screamed their intimidating presence right outside the offices of the President’s Chief of Staff, Vice President, and National Security Adviser, and North Portico, the commonplace thrived by selfie-clicking tourists on a normal day.

On the seventh night of the unabating protests, President Trump declared, “I am your President of law and order,” and threatened to deploy “thousands and thousands” of “heavily armed” military personnel to quash the protests. On his orders, officers fired rubber bullets and sprayed chemicals to disperse protestors outside the White House gates. Further, the protesters were intimidated by downwash of air, debris, and fuel exhaust from, twin-engine UH-60 Black Hawk and UH-72 Lakota helicopters barely flying above the tree line.

Analysis of Public Sentiments using Social Media Analytics

In such a scenario Social media analytics was conducted by gathering thousands of tweets from the Twitter platform and subjected to analysis. Stop words were declared and eliminated from text analysis to remove the clutter of unimportant words and the analyses focused on the words with high frequency instead. Stemming was also applied which is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma. Eventually, the resulting Bag of Words (BOW) was used for further analysis. BOW takes into account the words and their frequency of occurrence in the sentence or the document disregarding semantic relationships in the sentences.

The word cloud (Fig 1) reproduced below shows Racism, Police killings, back lives matter, Black discrimination as the main themes. The Twitter data was Auto coded to yield the sentiment analysis.

Fig 1: Word Cloud obtained from analysis of tweets from Twitter

Sentiment analysis which is also called “opinion mining” or “emotion AI” is a Natural Language Processing (NLP) technique to categorize text depending on whether it expresses an opinion and whether the opinion is positive, negative, or neutral. It is a powerful technique to understand the feelings of people towards a product, person or event. Sentiment analysis is a text analysis method that detects polarity (e.g., a positive or negative opinion) within the text of a whole document, paragraph, sentence, or clause. The sentiment index was calculated to be 0.192, which shows the event had generated anguish worldwide against racial discrimination and police brutality. The very intense negative sentiment of the public manifested in tens of thousands of people who defied curfews and took to the streets of cities coast to coast to protest over Floyd's death and brutality against other black Americans (Fig 2). Following Floyd’s death more than 350 cities in the US saw demonstrations from the public and National Guard troops, a component of the US Army, was deployed in at least 23 states to handle the sometimes-violent demonstrations.

 

 

 

 

 

 

Fig 2: Sentiment Analysis From the tweets

Fig 3: The geographical spread of originating Tweets on Twitter

 

As shown in Fig 3, the tweets came from across the world which was an indication the unrest was not contained to Minneapolis but was fast spreading to other countries. Most of the protests of the Black Lives Matter movement, tried to communicate strongly against the discrimination by displaying slogans like-Enough is enough. Stop killing us. Justice for George Floyd. The peaceful protests garnered support from across the continents.  The Trump Government softened its initial hardstand and tried to do damage control.  The day after Mr. Floyd’s death, the Police Department fired all four of the officers involved in the episode. On May 29, the Hennepin County attorney, Mike Freeman, announced third-degree murder and second-degree manslaughter charges against Derek Chauvin, the officer seen most clearly in witness videos pinning Mr. Floyd to the ground. 

 

The Black Lives Matter protest gave the world a rude jolt and triggered civic unrest in America not seen since the assassination of Martin Luther King Jr in 1968. The resistance was met by heavily armed police gushing cruisers into crowds, firing rubber bullets at reporters and beating citizens peacefully exercising First Amendment rights.

Quotes by influential Americans after the incident

 “Trump’s re-election chances are going down in flames. It’s hard to see how these riots don’t boost Joe Biden’s claim to be the Alka-Seltzer America needs to soothe its stomach right now.” Dan Eberhart, a Republican donor and Trump supporter.

 

Stuart Stevens, a Trump critic who served as chief strategist to 2012 Republican Presidential nominee Mitt Romney, says that Trump won in 2016 with 46% of the vote because nonwhite turnout declined for the first time in 20 years. “You can call them protests, but you could also call them nonwhite voter-turnout rallies,” Stevens says of the racial-justice demonstrations. “It’s hard to imagine anything that’s going to be more motivating.”

 

Learnings from the incident:

The last spoken words of George Floyd, “I can’t breathe!!” are metaphorical of the asphyxiation minorities are experiencing in the form of racial discrimination. It is ironic the Police which is bestowed with the responsibility of being the protector turns into the perpetrator of the crime. The administration was clueless about the sentiments of the common man and resorted to the measure of the last resort -The use of armed forces against protesting civilians. This is a clear indication the seats of power are unaware of the ground realities and are most insensitive to the feelings of people. This is a sorry state for any democracy. The then US President in his tweets equated the Black lives Matter protesters to looters. The response of the administration only served to escalate the tensions between the Police and minority community across the nation. The protests which spread like wildfire from Minneapolis to the country and crossed the border to other continents were not only a protest against police brutality but against the administration which has failed them on many counts.  Though slavery was abolished long time back and racial discrimination was declared illegal the incident is a pressing reminder that mindsets need to be changed. It takes Policy, Police, Administration, Law and most importantly, metamorphosis in mental attitude and to accept and respect that –“All Men are born Equal.”

Teaching Note  

Case Synopsis

The case setting is based on the real-life event when George Floyd, an African American, was suspected of having used counterfeit money to buy a pack of cigarettes. The case was reported to the police and three white policemen arrived on the scene. George Floyd was pinned to the ground and Police Officer Derek Chauvin pressed his keen on Floyd’s neck choking him. Floyd begged to be released but to no avail and was suffocated to death.  The event sparked widespread protests across America and soon spread to other parts of the world. The American administration miscalculated the sentiments of the common man and decided to use the army against them. This further increased the divide between the police and the protestors. Tainted tweets on the social media by President Donald Trump further escalated the anger of the protestors. The case used sixty thousand tweets collected from Twitter and qualitatively analyzed them using social media analytics.The case study endeavors to emphasize the need for taking cognizance of public sentiments in management and the use of social media analytics in understanding them.The case study adopts a holistic framework that combines sentiment analysis, emotion detection,  spatial/temporal analysis to understand the range of issues surrounding the # Black Lives Matter movement.

Key words: Black Lives Matter, Bag of Words (BOW), Lemma, Sentiment analysis, Social Media analytics, Sentiment Index, Stopwords, Qualitative analysis, Word Cloud

Target Audience

This case can be effectively taught to post graduate students in management and executives on topics such as Social media analytics, Sentiment analysis, sentiment index, Qualitative analysis and response strategy to civil unrest.

The learning objectives are as follows:

  • To use social media analytics to assess public opinion on a critical issue
  • To undertake sentiment analysis to assess the mood and feelings of people.
  • To craft a response strategy using sentiment analysis.

Assignment Questions

  • Do you think Social media is a good enough reflection of the sentiments of the public?
  • Can Social Media offer a potent platform for emotions or sentiments which can play a critical role in escalating a protest to a movement and then to a revolution?
  • What is your opinion about the way the US administration handled the Black Lives Matter Protest?
  • Do you think qualitative analysis to understand the sentiments of the protestors could have helped in a better response strategy by the administration?
  • In your opinion what could the administration have done to handle the situation better?

 

Teaching Plan

Time

(Duration)

Discussion Question/ Topics

 

10 Minutes

Introduce the context of the case

10 Minutes

Survey Students about their awareness of the case as it had unfolded

15 Minutes

Discuss the importance of understanding the sentiments of the people.

20 minutes

Discuss the relevance and the process of Sentiment analysis

20 minutes

Discuss the importance of Social media in shaping opinions and emotions.

30 Minutes

Initiate a group discussion to discuss the ways in which the Administration could have handled the situation.

 15 Minutes

Key learning points and conclusions

Total 120 Minutes

 

 

 

References:

 

 

 

 

 

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  • Mays, N. & Pope, C. (2000). "Qualitative research in health care: Assessing quality in qualitative research." BMJ. 320(7226), 50-52.

 

  • Patton, MQ. (1999). "Enhancing the quality and credibility of qualitative analysis." HSR: Health Services Research. 34 (5) Part II. pp. 1189-1208.

 

  • Patton, MQ. (2001). Qualitative Evaluation and Research Methods (2nd Edition). Thousand oaks, CA: Sage Publications.