#BigPharma Twitter Analysis

Posted on Fri, 09/06/2019 - 14:34 by capecod

Twitter Analysis Report

In the News: Pharmaceutical Companies

The last time you felt sick did you take medication? Do you know someone who has a chronic condition? Medication and the medical community are vital part to protecting our health. The pharmaceutical industry can have positive impacts in the community.

However, lately we have seen how the pharmaceutical industry has acted corruptly by interfering with politicians with lobbying, pushing for overprescribing medications when not needed, and setting prices so that people cannot afford their medications. People on Twitter have responded with their views of the pharmaceutical industry recently, and I wanted to investigate the main reasons for people's posts on Twitter about the pharmaceutical industry.

Methods

First, I used TAGS v6.1.9.1 to compile the tweets that I would analyze. I searched for tweets with the hashtag, #BigPharma. Initially, I had about 10,000 tweets enter the database and filtered to 119 tweets. To narrow the tweets down to be able to analyze the tweets comprehensibly, I filtered them based on date recorded from Monday August 26, 2019 to Tuesday August 27, 2019. Also, I filtered out the retweets since I wanted to see the content of the original tweets. Tweets that were in a different language were deleted from the database as well.

To analyze my tweets, I created six distinct categories. These categories included: what is the tweet doing, rationale for the tweet, stance, rhetorical appeals, evidence, and media. I found that the most useful categories to my research were what is the tweet doing, rationale for the tweet, and rhetorical appeals, as these demonstrate why the person is tweeting about pharmaceutical companies and how the person is making an argument. Most of the tweets had an opposed stance to the pharmaceutical companies, which made this category less beneficial to my analysis of the data. For evidence, there were many repetitive sources integrated into the tweets which also did not lead to a need to look at this category as extensively. Some of the tweets incorporated media but not as often as expected probably due to the nature of the topic. By sorting the tweets into categories, this helped me further my understanding of these tweets which I will discuss in the following section.

What Action is the Tweet Conveying?

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Figure 1. The action words to describe what the tweet is doing.

First, it is important to see what the Tweet is trying to do. People tweeting with the #BigPharma usually in the study are writing with a negative viewpoint. Figure 1 demonstrates that more than 40 of the tweets focus on bashing and mocking the pharmaceutical companies. It is interesting to see how people are more likely to reveal their negative thoughts than examine the situation.

According to Figure 1, there was a considerable amount of people, close to 20, who reported about the situation. Some people probably did this in order for their followers and for other people searching this hashtag to understand what is currently happening with the pharmaceutical companies. During the study period, Johnson and Johnson got charged for a lawsuit relating to the opioid epidemic. This probably spurred the increase of reporting tweets on the situation. I was surprised to see how there were almost 15 tweets each in Figure 1 that were either giving support or communicating how the pharmaceutical companies are finally being held accountable. People might have given this affirmation as a way to represent their viewpoint on the way the pharmaceutical companies have been operating recently. Overall, these tweets are opposed to pharmaceutical companies' actions but convey their sense of loathing in different ways.

Purpose of Composing the Tweet

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Figure 2. The categories for the rationale of the tweets.

Despite most of the tweets expressing discontent toward pharmaceutical companies, the respondents that I observed had varying purposes for using the #BigPharma hashtag. Figure 2 highlights how the majority of the tweets, around 50, related to how the pharmaceutical companies are corrupt. I knew this was a key factor in people's opposition to pharmaceutical companies, but I did not realize how people thought of that more compared to other issues stemming from the pharmaceutical companies. Also, Figure 2 demonstrates approximately 21 of the tweets consisted of people writing to represent how they are against drugs. This included some tweets for alternatives to drugs such as mental health, spirituality, natural medicine, and recreational drugs such as marijuana.

Also, Figure 2 showcases a sizeable amount of tweets associated with the  pharmaceutical companies interfering with the political sphere in this country. The political tweets refer to how people are frustrated with the politicians accepting funds from lobbyists who have interests associated with pharmaceutical companies. Many of these tweets are critical of political figures ignoring how prescription drugs and opioids are affecting people's lives. For instance, the tweet below from, Twitter user, Sarah Burris, expresses her frustration with President Trump and other political figures for not regulating the pharmaceutical industry but instead focusing on other matters such as building the border wall between Mexico and the United States. This demonstrates how people have discontent for the current deregulation stance on pharmaceutical companies which allows for them to have greater power in this country.

Lastly, I was surprised to see that few of the tweets in Figure 2 were focusing on how the pharmaceutical companies are affecting people with the high prices of drugs and drugs leading to negative side effects. Respondents in this data were focused more on the power this industry has in our country. There were a few tweets on personal experiences of how the pharmaceutical companies negatively impacted their lives by paying exorbitant prices for drugs that they needed to survive. For example, a tweet shown below from Twitter user, John Harris, explains his personal story of how his wife was prescribed by her physician antibiotics for years which have caused side effects for a cyst that should have been initially removed. These tweets empathizing with the people's experiences with this industry were less evident. It might be easier for people to attack the companies on their corruption and greater power in the political domain than to emotionally connect with the specific examples of what is occurring on a daily basis for many individuals.

How do the Tweets Appeal to the Readers?

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Figure 3. The percentage of each rhetorical appeal used in the tweets.

Rhetorical appeals play an important role in conveying the message and argument proposed in the tweets. Figure 3 presents that 61.1% of the tweets used pathos which is a common rhetorical form on Twitter. Relying on emotions and feelings enables a reader to feel more connected and invested in the topic. People would display pathos by using language that expresses dissatisfaction with the pharmaceutical industry.

Figure 3 indicates that 25.9% of respondents incorporated ethos into their tweets. Many of these tweets came from medical professionals, public health experts, and news sources. Doctor Ali Khan's tweet shown below uses his platform as a credible source for people to fully understand the current situation with the Johnson and Johnson Company court case. This hashtag and others with ethos in the study tried to dispel the myths related to this industry and to provide accurate information. Ethos is more necessary for this argument on Twitter since people want to listen to people with more credibility and knowledge on this topic.

According to Figure 3, logos at 13.0% was the least used rhetorical appeal in the corpus. The evidence would include links to news sources discussing the Johnson and Johnson company being sued and from GlobalData Healthcare on the impact of tax reform for these pharmaceutical companies expanding. I noticed for the tweets that featured logos that there was repetitive evidence used. It appeared that many people copied what others had said instead of investigating other evidence and sources to include in their tweets. Possibly, people trusted what others had included as evidence instead of  critically reading the sources and trying to search for additional facts on the topic. 

Conclusion

One limitation of this analysis is not having a reliable way to measure people's demographic information such as race, gender, age, and political affiliation. For instance, it would have been interesting to identify people's political affiliation. This would have created more meaning to understanding the composition of people's tweets. Assuming what the people's political affiliation is based on their profiles might create a bias on the analysis. Another limitation of this analysis involves having a smaller corpus of tweets. Having more tweets would have allowed for greater complexity of the data and more viewpoints to be present.

Overall, this analysis of the hashtag, #BigPharma, highlights the benefit of arguing on the internet with Twitter. Even with more serious topics, Twitter can serve as a platform for all people to engage in discussion on a certain issue.  In regards to the hashtag, #BigPharma, this study demonstrates how most people were arguing against the pharmaceutical industry. The respondents mainly composed tweets on #BigPharma to highlight the corruption that is occurring compared to detailing the more personal effects created from this industry. Many tweets displayed pathos and some ethos to express their viewpoints and to further their arguments on the issue.