Digital Planet > Off the Charts > The Elon Musk—Twitter Takeover Saga: A Multi-Country Sentiment Analysis of Twitter Users

April 29, 2022

The Elon Musk—Twitter Takeover Saga: A Multi-Country Sentiment Analysis of Twitter Users

Summary: In our analysis of over 4 million tweets during an 8-week period (March 1st – April 27th, 2022) across 5 countries— the United States, United Kingdom, India, South Africa, and Nigeria— we studied the sentiments expressed by users on the platform to Elon Musk’s Twitter takeover saga. While reactions to Musk’s initial criticism of the platform were generally more positive, his plan to buy Twitter was met with largely negative emotions.

On April 25th, Elon Musk, the richest man on earth, bought Twitter for $44 billion. One month before, in late March, Musk began publicly criticizing Twitter, saying he was toying with the idea of starting his own social media company, tweeting a poll asking if the Twitter algorithm should be made open source (82.7% of respondents said yes), and polling users on whether they believed Twitter adheres to free speech (70.4% said no). From there, a wild ride followed: Musk announced a 9.2% stake in the company; Twitter offered him a board seat; Musk offered to buy the company; the board’s “poison pill” defense failed; and finally Twitter and Musk agreed to a takeover deal.

Through our analysis of over 4 million tweets during an 8-week period (March 1st – April 27th, 2022), we tracked the reactions to this evolving saga of a set of stakeholders who may be most impacted by the news: Twitter users themselves.

The global reaction was mixed at first, with some negative and largely positive emotions emerging while Musk embarked on a public criticism of the company, but reactions turned more polarized when he announced his 9.2% stake. His offer to buy the company and the announcement that it went through were met with decidedly negative emotions.

A more nuanced picture emerges from our multi-country analysis on the timeline. Below, we have disaggregated the negative and positive sentiments into the Six Basic emotions: surprise, sadness, joy, fear, disgust, and anger. When interpreting the charts, it is important to note that while there are times where joy alone appears the be the dominant emotion expressed, the sum of more negative emotions like sadness, fear, disgust, and anger taken together paint a different picture of negative reactions outstripping positive ones. Indeed, across all countries studied, when the final takeover deal was announced, negative expressions exceeded positives.

In the US, Musk’s offer to buy Twitter was met with a burst of sadness and anger.

Like in the US, users from the UK reacted to the news of Elon’s offer to buy Twitter with sadness and anger, and replayed those emotions when reports emerged that the company was nearing a deal with Musk.

In India, Musk’s announcement of his 9.2% stake generated a spike of joy whereas his offer to buy Twitter was met with a mix of joy, anger, and sadness. While joy spiked at the announcement of the finalized deal—more so than in any other country we studied—taken together, the more negative emotions (sadness, anger, and fear) dominated Indian Twitter for the day.

Reactions in South Africa, Musk’s country of birth, were similar to India’s, in the sense that the 9.2% stake announcement was met with joy and positivity, but as a whole, negative emotions took over as Musk’s true intentions became more apparent.

Nigeria’s sentiment graph is like India’s and South Africa’s. A significant spike in joy when Musk announced his stake led to a more complex picture as events unfolded, with negative emotions dominating the day when Musk offered to buy the company and on the day the takeover deal was finalized. Yet, during those same events joy still showed significant spikes, indicating polarization among the Nigerian Twitter community.

What patterns and insights do you see in these charts? Share your thoughts with us on Twitter @dgtl_planet


We use specialized technology and algorithms to access and analyze large amounts of open-source data. For the purpose of this analysis, we have assembled a dataset of over 4 million Tweets, between March 1 and April 27, 2022, related to the Elon Musk’s Twitter takeover attempt. Furthermore, we have analyzed sentiments and emotions across 5 countries: US, UK, India, South Africa, and Nigeria.

We have cleaned up the dataset to exclude spam tweets and other irrelevant topics. Given the high-visibility of @elonmusk, there are many “hijackers” that tag this handle to gain visibility for their tweets—regardless of topic. Most of these spam topics were related to crypto/NFTs and the Ukraine-Russia conflict.

We follow four broad steps to conduct data driven analysis as shown in the below exhibit.

These four broad steps encompass different processes and analysis techniques which allows us to conduct an in-depth analysis of the collected data. Some of the key analysis methods apply as part of this research study are as described below:

Longitudinal tracking

We conducted time-series analysis for various aspects of our data set. For example, we examined trends for the volume of conversations and sentiment and emotion mappings. These trend analyses can be conducted on various time intervals including hours, days, weeks, and months. For this study, we have aggregated data every week and, in some cases, daily.

Sentiment analysis​

Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis, and computational linguistics, to systematically identify, extract, quantify, and study affective states and subjective information. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence, or an entity feature/aspect is positive, negative, or neutral. Through our Natural Language Processing algorithms, we can tell whether a particular message is:

  • • Negative (e.g., the user was angry, disappointed, or just had a bad experience with another post, topic or online user),
  • • Positive (e.g., the user was happy and positive towards a certain topic, post or online user),
  • • Neutral (e.g., the user was conversing about less critical issues or gives non-emotional feedback on a particular topic or user).

Emotion analysis​

We share our opinions about all kinds of things online, but we also share how we feel. Emotion analysis is similar to sentiment analysis. However, it is more nuanced and can unpack both positive and negative sentiments into six underlying emotions. For the analysis, we used Seismic, an internal tool at Ripple Research that categorizes social mentions by emotion depending on their content. We use the list of six basic emotions as defined by the famous psychologist Paul Ekman to classify mentions:

  • • Anger
  • • Disgust
  • • Fear
  • • Joy
  • • Surprise
  • • Sadness

By using a custom statistical NLP classifier our algorithm automatically assigns emotions to text.

Trending topics identification​

Finding trending subtopics related to or under a broad topic can be an important tool to understand what narratives are driving audience opinion. This analysis also shows sub-topics that “fade away” and sub-topics that tend to be “sticky”.

Demographic insights

​Insights into demographics let us learn about the people behind a conversation. We can extract demographic data from Twitter and can analyze the people within our dataset both as a group and as individuals, including their:

  • • Account type (whether they are an organization or an individual)
  • • Gender
  • • Interests
  • • Profession
  • • Location

A brief note here—we can only extract those data points which are self-mentioned.

Engagement pattern identification​

Engagement patterns are useful to identify when and how the audience interacts with the research topic. Through this, we can gain additional insights on online activity, day of the week, and even time of the day when the activity is high.

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