BBS301: Develop a Research Statement that Captures the Problems Identified in the Case: Applying Mixed Methods Research to Business Assignment, MU, Singapore

Your Task

Using the Business Research flowchart (below) answer the following four questions

QUESTION 1

Summarise the research objectives. Develop a research statement that captures the problems identified in the case.

QUESTION 2

Identify and explain the research technique and method used to answer the research objectives.  Highlight why this approach is superior to other options.

QUESTION 3

Describe the characteristics of the sampling design, data gathering, and data analysis approach. List two limitations of this approach and how they may have been minimized

QUESTION 4

Describe at least one ethical dilemma presented by this case and what approach you would take to address it for future studies.

CASE STUDY 

How Trump impacts harmful Twitter speech: A case study in three tweets

Megan Brown and Zeve Sanderson (October 22, 2020)           

Source: https://www.brookings.edu/techstream/how-trump-impacts-harmful-twitter-speech-a-case-study-in-three-tweets/

Last weekend, Tori Saylor, Michigan Governor Gretchen Whitmer’s deputy digital director, watched as President Donald Trump used yet another rally to attack her boss. She knew what would come next: “I see everything that is said about and to her online. Every single time the President does this at a rally, the violent rhetoric towards her immediately escalates on social media. It has to stop. It just has to,” Saylor tweeted.

Saylor was describing a dynamic that has now become familiar to researchers of online speech: Offensive speech on the internet tends to arise in response to political events on the ground. After Trump has attacked his opponents at a rally or other event, his online followers have, in some cases, taking that as a cue to attack those same opponents. For the president, it provides a useful amplifying tool. For the opponents being targeted, it represents a nightmare of online harassment.

But what about Trump’s online speech? Just as he targets his opponents in rallies and speeches, he also takes to Twitter to dole out criticism and ad hominem attacks. Here, we examine three recent tweets from the president and whether his tweets have a similarly negative impact on the quality of other online speeches. These three tweets offer a case study in how elite speech online can impact the incidence of harmful speech. The tweets in question are not obviously threatening in nature—they fall into a well-documented trend of Trump attacking politicians on Twitter while remaining in-bounds of platforms’ content moderation policies. But that does not mean that they do not impact the overall quality of online discourse. Our findings highlight the challenges platforms face as they define their content moderation guidelines and systems in the lead-up to, and aftermath of, the election.

Consider the following presidential tweets:

…today that they foiled a dangerous plot against the Governor of Michigan. Rather than say thank you, she calls me a White Supremacist—while Biden and Democrats refuse to condemn Antifa, Anarchists, Looters, and Mobs that burn down Democrat-run cities…

— Donald J. Trump (@realDonaldTrump) October 9, 2020

…I’m playing for your guns, and I’m playing for your values. For all the Federal Employees in Virginia, remember, it was me that got you the Federal Pay Raises, not Sleepy Joe Biden. I’ll be having a Big Rally in Virginia, to be announced soon! https://t.co/WwzdPhDkAZ

— Donald J. Trump (@realDonaldTrump) September 18, 2020

So weird to watch @FoxNews interviewing only failed Dems, like Representative Tim Ryan of Ohio, who got zero percent in his recent presidential run, and following Sleepy Joe’s train to wherever. What a difference from the past – But we will win anyway

Collecting and classifying tweets

For each of the three politicians mentioned in Trump’s tweets, we complete a three-step analysis on a random sample of tweets. (Every day, the NYU Center for Social Media and Politics collects a 10% random sample of Twitter, which amounts to tens of millions of daily tweets.) First, we use keywords to identify the corpus of tweets associated with each of the three politicians Trump mentioned. For each politician, we used the politician’s name, Twitter handle, and title. To be included in the corpus for Virginia Gov. Ralph Northam, for example, a tweet had to include “Governor of Virginia,” “Governor of VA,” “@GovernorVA,” “Northam,” or “Ralph Northam.”, we estimate the levels of two characteristics in that corpus: severe toxicity and threats.

We do so by using Perspective, an open-source API created by Jigsaw and Google’s Counter Abuse Technology team to enable the classification of harmful speech online. Perspective defines severe toxicity as “very hateful, aggressive, disrespectful” speech, and threats as describing “an intention to inflict pain, injury, or violence against an individual or group.” Third, we construct an interrupted time series with a 24-hour moving average, which enables us to isolate the impact of Trump’s tweet on the levels of severe toxicity and threats. Taken together, this analysis provides a birds-eye view of how Trump’s recent tweets impact the larger online discourse around the political figures he attacked.