This document summarizes a study analyzing how Elon Musk's acquisition of Twitter in late 2022 impacted the tweeting behaviors of self-described entrepreneurs. The study found that most entrepreneurs (over 50%) did not change their tweeting patterns in response to the acquisition and can be considered "Musk-donotcares." A small percentage (under 2%) increased references to Musk and engagement and are termed "Musk-lovers." An even smaller group (under 4%) decreased Twitter usage and are termed "Musk-haters." Overall, the study found the "Musk effect" to be largely a "Non-Musk effect" as most entrepreneurs were unaffected in their Twitter behaviors.

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The “Musk” Effect at Twitter
Dmitry Zinoviev*
, Arkapravo Sarkar†
, Pelin Bicen†
*Mathematics and Computer Science Department, †Department of Marketing
Suffolk University, Boston, U.S.A.

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Who’s Elon Musk?
● Elon Musk, the Chief Executive Officer of Tesla and SpaceX,
a South African, is a prominent figure recognized for his
unconventional personality and controversial claims.
● His actions and statements have frequently incited
substantial fluctuations in the stock market, the
cryptocurrency market, and notably within the technology
sector.
● Despite this, the influence of Musk's conduct and actions on
social media, specifically pertaining to the social media
utilization and behavior of other entrepreneurs, remains
largely unexplored in academic literature.

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The Twitter Acquisition Saga Timeline
● January 31, 2022 (Day 1)
● Musk commences the acquisition of Twitter stock
● April 14, 2022 (Day 73)
● Musk extended an offer to Twitter for the procurement of the company
● July 8, 2022 (Day 158)
● Musk announces his intention to discontinue the acquisition
● October 27, 2022 (Day 269)
● Agreement between Musk and Twitter finalized

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R/Q: Do Entrepreneurs Care About the Acquisition?
● Have they changed their twitting patterns?
● Have they changed their twitting topics?
● Who are those who did? (If any.)

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Data Set
● Acquired through Twitter Academic account (now defunct)
● Self-described entrepreneurs and professionals (used one or more descriptors in bio):
● Entrepreneur, owner, founder, co-founder, business owner, CEO, co-owner, representative,
consultant, assistant, analyst, officer, partner, developer, head, coordinator, specialist, executive,
manager, managing director, chief executive, director, VP
● From officially English-speaking countries (the USA, Great Britain, Canada, Ireland, New
Zealand, Australia) and South Africa
● Descriptive statistics:
● 47,189 unique users
● 12,176,276 tweets
● 81 weeks from October 1, 2022 to April 21, 2023

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Gendering
● A part of another project (in progress).
● Applied heuristics to infer gender, where possible
● Gendered names, usernames, pronouns, family roles, and occupations about 50%
→ about 50%
● Trained logistic regression (deps: IsMale? IsFemale? IsNonbinary? IsCompany?)
● Applied to the rest of the corpus
● Only accepted results if exactly one dependent variable predicted to be True
● Precision 99.3% (wow!)

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Estimating Political Leaning
● From the list of accounts being followed
● At most 1,000 “followees” collected for
each user
● Community detection in the the network
of co-following
● Six large clusters: “liberal,” “conservative,”
“business/tech,” “entertainment,” “South
African,” “liberal entertainment”
● Leaning = # of “liberal” followees - # of
“conservative” followees
● So much to be done!

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Fixing Timeline Non-Uniformity
● Twitter allows allowed to obtain at most 3,000 most recent tweets
● Shorter timelines for active users
● (Bad) solution:
● Exclude all users whose timelines do not protrude to the beginning of the saga (22%)
● The dataset biased towards medium- and low-activity accounts
● New descriptive statistics:
● 36,828 (78%) unique users
● 4,034,007 (39.5%) tweets
● 56 (69%) weeks

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Measuring the Twitting Pulse
● Number of tweets per week per person
● Thick red line—mean twitting
frequency
● Overall decline at the times of
uncertainty
● Stabilization at inevitability
● Slow decline after the acquisition
● Merry Christmas!

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References to Musk
● Count direct mentions @elonmusk
● Count hashtags #elonmusk, #elon, and
#musk
● In general, the numbers are LOW
● The spike caused by the acquisition was
short (two months)

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Classifying Users (1)
● Measure twitting activity at, a week and
two weeks before, a week and two weeks
after, and between the notable events
● 15-dimensional vector for each user
● K-means clustering to calculate
“tweeting trajectories”
● 8 clusters (trajectories)
● Arranged in descending order of cohort
size

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Classifying Users (2)
● Six trajectories are horizontal, differ only
in the mean frequency
● Did not respond to the change of
ownership!
● “Musk-donotcares”
● Trajectories #5 and #8: “deserters” from
Twitter
● “Musk-haters”
● Trajectory #7:
● “Musk-lovers”
● 1.9% of the corpus

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Looking at Hashtag Topics
● 1,640 most frequently used hashtags
extracted from 67.7% of the tweets
● Arranged into semantic topics
● Community detection in the network
of co-occurrences
● Top nine topics shown
● Spikes at:
● Merry Christmas!
● FIFA World Cup 2023
● No Musk detected!

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Who Cares? (1)
● Use logistic regression to explain user’s
assignment to each of the eight trajectories
● Independent variables (personal):
● Country
● Gender
● Political leaning
● Independent variables (Twitter-related):
● Account age
● Verified? (The “old” “blue bird”)
● Has URL?
● Bio description length
● # of followers
● # of following
● # of listed
● # of tweets

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Who Cares? (2)
● The independent variables that significantly explain the membership
● “Musk-haters” (#5, #8; 3.9%)
● Not from UK or Ireland; marginally significantly from the USA
● Newer accounts, longer bio descriptions , have URL
● More tweets, fewer listed, more followers, fewer following (“writers,” “opinion leaders”)
● “Musk-lovers” (#7; 1.9%)
● Male, conservative-leaning, from the USA, Canada, and New Zealand
● Newer accounts, longer bio description, have URL, verified
● More tweets, more followers, fewer following (“writers,” “opinion leaders”)

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Who Does Not Care?
● “Musk-donotcares” (#1; 56.5%)
● Not from Canada or the USA; from South Africa (?)
● Older accounts, shorter bio description, do not have URL, not verified
● Fewer tweets, fewer listed, fewer followers, more following (“passive readers”)

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Conclusion
● The “Musk” effect is a “Non-Musk effect
● Preprint at https://arxiv.org/abs/2304.11272