Negative and Misleading Posts Driving Critical Discussions on X

social media
information epidemiology

InfoEpi Lab

Information Epidemiology Lab


October 24, 2023



This report presents the findings of a content analysis for items posted on X (formerly Twitter) between October 7, 2023, and October 20, 2023, collected on October 20. The three datasets focused on different topics: Israel-Hamas, Ukraine, and vaccines. Each dataset had keywords and a filter to exclude tweets below a certain threshold. For more information about the definitions and filters, see the Appendix.

Some tweets were originally posted outside this date range but were retweeted or quoted between October 7 and October 20

The dataset can be found on the Harvard Dataverse. For a simpler version of this report, see the brief.

Key Findings

Tweets from accounts in all three datasets–henceforth known as trio-authors–appear significantly more negative in sentiment when compared to the General data. The difference between the high-performance tweet groups’ sentiment scores and the General sample was statistically significant (p-value = 8.912e-05).

Figure 1: Comparison of sentiments in the general post sample (General), authors with one high-performance tweet (In-One), authors with high-performance tweets on two topics (In-Two), and accounts that had high-performance tweets in all three datasets (Common).

This aligns with past research findings that negative words and sentiments increase click-through rates and that partisans similarly use negative emotions to increase engagement with followers. The trio-authors include 37 accounts that posted 579 high-performance tweets.

In addition to posting more negative content on average, some trio-authors selectively presented facts, used emotionally charged language, promoted us-versus-them or good-versus-evil framing, encouraged distrust or grievance and sometimes shared misleading or entirely false information.

With the platform owner’s stated “[wish] to become an accurate source of information,” these findings suggest the current approach has failed. Many of these accounts shared blatantly false information related to Ukraine, Israel, Hamas, vaccines, or have in the past. This demonstrates that these falsehoods reach millions of affected people at critical moments.

The engagement received by certain figures was unsurprising, like that of Israel’s official account in the wake of an attack by Hamas. Other accounts with no apparent connection, experience, or informed insight into the situation sometimes exceeded the engagement and impressions of credible media outlets and relevant leaders. This highlights a vulnerability that could easily be weaponized to threaten public health and national security.

Impressions and Engagement for Authors in All Three Datasets

A more in-depth discussion of narratives and themes from this report can be found in the section Narratives and Themes. Broadly, many claims did not fall on a true-false spectrum but instead leveraged the situations to advance a worldview or ideological issue.

  • Tweet (2,568,593 impressions): “In the past six hours, I’ve seen more war footage out of Israel than in the entirety of the”war” in Ukraine. Why is that?“

  • Tweet (1,634,256 impressions): “But why do you need an AR-15 with 30 rounds?!” Because the world just watched Hamas go door-to-door slaughtering unarmed Israelis. The government won’t always be there to protect you so you must protect yourself.

Misleading and even blatantly false content appeared in every dataset, which is noteworthy given that the data collection excluded tweets below a certain threshold. The exclusion means that we know that many users saw or engaged with these posts.

False Claims

Here are some examples of false claims.

  • Tweet (1,124,170 impressions): “Health Canada has confirmed the presence of a Simian Virus 40 (SV40) DNA sequence in the Pfizer COVID-19 vaccine, which the manufacturer had not previously disclosed” “The polyomavirus Simian Virus 40, an oncogenic DNA virus, was previously removed from polio vaccines due to concerns about a link to cancer.

  • Tweet (2,368,519 impressions): Mayo Clinic quietly updates website to say Hydroxychloroquine can be used to treat Covid patients Doctors were fired and censored for saying this Media smeared it All because Big Pharma couldn’t have any therapeutic drugs available in order to make billions from vaccine EUA

  • Tweet (1,726,823 impressions): Trudeau regime puts Canadian detective on trial for investigating link between infant deaths and mRNA vaccines

In one example from the Israel-Hamas dataset, an influential user posted:

BREAKING: Israeli Commander Nimrod Aloni has been captured by Hamas in the ongoing war. Analyst: “This is big. Israel is meant to be a big military force. This shows the extent to which they are on the back foot and not ready to respond to this attack. This also shows the extent of the Hamas operation”

Nimrod Aloni is the commander of the Depth Corps

AP later refuted the claim after it spread widely, and recent footage of Aloni has been shared by the IDF on X. While the IDF-affiliated account received a little over 25,000 impressions. The post received over 18,000,000. The AP fact check came two days after the post on X. The post remains live as of October 23, 2023.

Account Engagement

Table 1: This dataset included tweets related to Israel and Hamas
Author Total Engagements Total Impressions
jacksonhinklle 19218252 679973893
CensoredMen 4925487 369476565
Israel 3526237 459500344
MuhammadSmiry 2594957 68324877
POTUS 2537765 340652078
Lowkey0nline 2512884 179758327
IDF 2428147 181347814
SaeedDiCaprio 2330893 108699755
DrLoupis 2214116 94858821
Timesofgaza 2138036 157367086
omarsuleiman504 1924753 72284909
AlanRMacLeod 1429264 61762418
elonmusk 1394980 195852974
sahouraxo 1356829 42989307
visegrad24 1296797 166124915
CollinRugg 1231792 185504815
benshapiro 1221677 113066349
m7mdkurd 1057071 41099224
spectatorindex 1021016 126048849
PopBase 938309 273961685
netanyahu 936738 71270721
TheMossadIL 936563 40986864
narendramodi 918653 62142798
HoyPalestina 915786 34707055
Benzema 874857 49135364
Table 2: This dataset contains keywords related to the Russian invasion of Ukraine.
Author Engagements Impressions
jacksonhinklle 3531000 127852680
ZelenskyyUa 1196890 65930171
DrLoupis 820074 47488485
DefenceU 607144 25701067
PicturesFoIder 518568 116221609
Gerashchenko_en 504582 26163713
visegrad24 466111 27457934
JoeyMannarinoUS 417685 17728778
WallStreetSilv 331054 66026305
KyivIndependent 328912 23100337
Byoussef 327366 21190268
IAPonomarenko 298860 30457140
GuntherEagleman 287998 5264082
JayinKyiv 283543 12823311
KimDotcom 243281 20518243
PopBase 241346 13360457
reshetz 238600 6248712
Partisangirl 235884 7204375
RealScottRitter 233739 6935045
bayraktar_1love 209168 24171150
Maks_NAFO_FELLA 198582 6716324
WarClandestine 198556 10394227
AlanRMacLeod 185633 5997330
RepMTG 172240 6050145
MarinaMedvin 170372 11632313
Table 3: This dataset includes tweets matching vaccine-related keywords or words associated with vaccine discussions online
Author Engagements Impressions
DiedSuddenly_ 1514695 66681704
VigilantFox 546347 27888914
iluminatibot 492601 21401507
LeadingReport 385857 16254116
Resist_05 376731 27704353
TheChiefNerd 353561 42440280
robinmonotti 284548 14285553
vivekagnihotri 280024 5100486
P_McCulloughMD 269386 8772643
DC_Draino 269093 22988051
kevinnbass 265839 12723592
DrLoupis 251139 9625041
PeterSweden7 215835 5230555
WallStreetSilv 209764 34754914
CollinRugg 182827 25632320
Inversionism 166138 8052192
MattWallace888 163056 15187004
MakisMD 161204 8534635
TexasLindsay_ 155928 8892411
RWMaloneMD 140618 4190681
NobelPrize 139554 20671549
liz_churchill10 136745 2508441
goddeketal 136117 8191162
elonmusk 127301 5722954
Travis_in_Flint 125725 7751633

One of the most striking findings in this analysis was how significantly a single user could outperform the most relevant voices. Although the account owner has worked in national grassroots campaigns, there’s no apparent personal connection, education, or work history related to the topics they now dominate.

InfoEpi Lab defines the most relevant voices as credible media, community leaders and citizens from the affected area, relevant experts, humanitarian workers, and elected officials or agencies involved in responding to the situation.
  • Despite this, this individual outperformed the heads of state in the Israel-Hamas and Ukraine datasets, posting 292 high-performance tweets. In total, they garnered 22.7 million engagements and 807.8 million total impressions.

  • President Volodymyr Zelenskyy of Ukraine received 1.1 million engagements over the two weeks, while this account received over 3x the number of engagements related to Ukraine.

  • Exceptional tweets from this user were not limited to the topic of Ukraine. They posted more tweets receiving 20,000 or more likes than the Israeli state, the Israel Defense Forces, and the President of the United States combined.

The account owner has a history of promoting and appearing on Russian state media, spreading misleading claims about events, and casting doubt on specific incidents.

In April 2022, the account owner raised questions about the Bucha Massacre:

“How did forces kill 410 civilians in Bucha, Ukraine after they had completely left Bucha, Ukraine days earlier?”April 4, 2022

“When it comes to the Bucha massacre, facts are not the friends of the Ukrainian narrative.”April 6, 2022

The situation captured in the Israel-Hamas dataset is particularly concerning. Even as the country was dealing with a lethal attack, posts from this account overshadowed community leaders, people affected by the situation, credible sources of information, and relevant experts.

Author Rankings

Table 4: Top 25 authors of tweets in the Israel-Hamas dataset
Author Frequency
jacksonhinklle 225
CensoredMen 74
Israel 61
MuhammadSmiry 56
IDF 46
DrLoupis 38
Lowkey0nline 33
Timesofgaza 30
visegrad24 30
spectatorindex 28
sahouraxo 27
omarsuleiman504 25
AlanRMacLeod 24
CollinRugg 24
TheMossadIL 21
benshapiro 19
HoyPalestina 17
m7mdkurd 17
JoshuaPHilll 15
ShaykhSulaiman 15
HananyaNaftali 14
Mediavenir 14
agusantonetti 13
AvivaKlompas 13
Table 5: Top 25 authors of tweets in the Ukraine dataset
Author Frequency
KyivIndependent 104
DefenceU 79
ZelenskyyUa 79
JayinKyiv 69
bayraktar_1love 68
jacksonhinklle 67
Gerashchenko_en 66
PulseOfUkraine 51
reshetz 48
visegrad24 47
front_ukrainian 45
strategywoman 41
NOELreports 36
wartranslated 31
JoeyMannarinoUS 29
maria_drutska 28
GlasnostGone 27
EuromaidanPress 24
DrLoupis 23
DPRIANarchive 22
IAPonomarenko 22
nexta_tv 22
GuntherEagleman 20
igorsushko 20
Table 6: Top 25 authors of tweets in the vaccine dataset
Author Frequency
DiedSuddenly_ 55
P_McCulloughMD 40
MakisMD 31
iluminatibot 25
VigilantFox 25
vivekagnihotri 25
kevinnbass 21
Inversionism 20
LeadingReport 20
TheChiefNerd 19
wideawake_media 15
bambkb 14
PeterSweden7 14
ABridgen 12
JimFergusonUK 12
liz_churchill10 12
Resist_05 12
robinmonotti 12
DrLoupis 11
RWMaloneMD 11
stkirsch 10
IamBrookJackson 9
Cernovich 8
SHomburg 8
EricTopol 7

Sentiment Analysis

Figure 2 shows violin and internal box plots of the sentiment scores (scored from -1 to 1). Each plot represents a specific group: General, In-One, In-Two, and Common. General is a sample collected using the Israel-Hamas dataset keywords with no filter, meaning it collected all tweets that matched one keyword. The collection took place on October 7 and October 13. The tweets were collected without selection beyond the keywords, giving a better idea of the range of views and sentiments expressed on a specific topic.

Figure 2: Comparison of sentiments in the General post sample (General), authors with one high-performance tweet (In-One), authors with high-performance tweets on two topics (In-Two), and accounts that had high-performance tweets in all three datasets (Common).

When more accounts express a similar sentiment, that graph will appear wider. As it narrows, that indicates fewer posts with that sentiment score.

  • The General group displays a relatively uniform distribution, with a concentration around the median, which appeared near or at zero. The concentration around zero means that the most common sentiment score was neutral or near-neutral.
  • The In-One group, which reflects posts from authors with a high-performance tweet in only one of the three topics, has a less pronounced concentration around neutral sentiment, although it is still the most common.
  • The In-Two group has a similar pattern to In-One but with a slightly narrower peak, suggesting a bit less concentration around the median. Here, the scores are slightly more polar, with fewer neutral posts.
  • The Common group shows a somewhat bi-modal distribution, with peaks near the median and lower sentiment scores. This pattern suggests the presence of two distinct subgroups within this category: one with neutral sentiment and another with more negative sentiment.

Differences were greatest between the General and Common groups.

Figure 3: Sentiment distribution of posts from General and Common datasets.
Warning: package 'ggplot2' was built under R version 4.3.2

# Combine the data frames
plot_data <- rbind(df_common_all, df_common_two, df_single, df_general)

# Reorder the factor levels of 'author_group'
plot_data$author_group <- factor(plot_data$author_group, 
                                 levels = c("General", "In-One", "In-Two", "Common"))

brand_colors <- c("General" = "#B35788", "In-One" = "#6D7987", "In-Two" = "#C8E9E9", "Common" = "#99BADD")  

# Adjust the annotation_custom() function call with these variables
p <- ggplot(plot_data, aes(x = author_group, y = sentiment, fill = author_group)) +
  geom_violin(trim = FALSE) +
  geom_boxplot(width = 0.1) +
  scale_fill_manual(values = brand_colors) +
  labs(title = "Sentiment Distribution by Author Group",
       x = "Author Group",
       y = "Sentiment Score") +
  theme_minimal() +
  theme(legend.position = "none",  # Hiding the legend as the labels are self-explanatory
    plot.background = element_rect(fill = "white", color = NA),  # Your brand background color
    panel.background = element_rect(fill = "white", color = NA),  # Consistency with plot background
    text = element_text(color = "#6D7987"),  # Your brand text color
    axis.title = element_text(size = 11, color = "#6D7987"),  # Adjusting size and color of axis titles
    plot.title = element_text(hjust = 0.5, color = "black", size = 11)  # Centering and setting the title color and size
# Display the plot


# Extracting the 'sentiment' columns from each data frame
sentiments_common_all <- df_common_all$sentiment
sentiments_general <- df_general$sentiment

# Creating the combined data frame
combined_data <- data.frame(
  sentiment = c(sentiments_common_all, sentiments_general),
  group = c(rep("Common", length(sentiments_common_all)), 
            rep("General", length(sentiments_general)))

brand_colors <- c("Common" = "#99BADD", "General" = "#B35788")  

ggplot(combined_data, aes(x = group, y = sentiment, fill = group)) +
  geom_violin(trim = FALSE) +
  scale_fill_manual(values = brand_colors) +
  labs(title = "Sentiment Distribution of Data from General and In-All-Three",
       x = "",
       y = "Sentiment Score") +  # Added a label for clarity, modify as needed
  theme_minimal() +
    legend.position = "none",  # Hiding the legend as the labels are self-explanatory
    plot.background = element_rect(fill = "white", color = NA),  # Your brand background color
    panel.background = element_rect(fill = "white", color = NA),  # Consistency with plot background
    text = element_text(color = "#6D7987"),  # Your brand text color
    axis.title = element_text(size = 11, color = "#6D7987"),  # Adjusting size and color of axis titles
    plot.title = element_text(hjust = 0.5, color = "black", size = 11)  # Centering and setting the title color and size

[1] "Common sentiments - Mean: -0.0533711331844772 Median: 0 Mode: 0 Standard Deviation: 0.309462099890748"
[1] "General sentiments - Mean: -0.0261762691433522 Median: 0 Mode: 0 Standard Deviation: 0.244751182937714"

Narratives and Themes

The themes across the three datasets (Israel-Hamas, Ukraine, and Vaccine) share several similarities, particularly in the areas of distrust, conspiracy theories, polarization, and calls for action. However, they also have distinctive elements rooted in the specific contexts of each topic.

A more in-depth narrative analysis can be found in the Appendix section Narratives
  • Distrust and Conspiracy Theories: High-performance tweets across the three datasets carried a strong current of distrust towards authorities or established systems. Whether it was skepticism towards the US administration and global institutions (Israel-Hamas), doubt over financial aid (Ukraine), or mistrust in COVID-19 vaccines and health authorities (Vaccine), distrust was there. Conspiracy theories were common in all three categories. The stories suggested hidden agendas or concealed truths in global politics, financial aid, or public health initiatives.

  • Polarization and ‘Us vs. Them’ Mentality: The “us vs. them” narrative is a common thread, creating divisions between different groups. This is seen in the Israel-Hamas context’s polarization between different geopolitical factions. With Ukraine, the discussion was more about internal and international political divisions. In the Vaccine dataset, this manifested in a division between those who trust the vaccines and health authorities from those who don’t.

  • Calls for Action Against Injustice: Across all topics, there’s a strong call to action and a sense of urgency to address perceived injustices. This might involve challenging leadership decisions, questioning financial aid, or demanding transparency and safety in public health interventions.

  • Global Impact: Each theme acknowledges the broader, global implications of the issues at hand, whether it’s international conflict, financial decisions affecting multiple countries, or a worldwide public health crisis.

  • Specific Focus of Distrust and Conspiracy Theories: While distrust and conspiracy theories are common, their focus varies. In the Israel-Hamas dataset, conspiracy theories revolve around geopolitical conflict and international relations. Related to Ukraine, they’re more about financial aid and corruption. For the Vaccine dataset, stories concentrate on public health, vaccine safety, and the potential ulterior motives of global health authorities.

  • Cultural and Ethical Considerations: The Israel-Hamas dataset uniquely highlights the role of religious and ethical considerations in shaping narratives. This aspect is less prominent in the Ukraine and Vaccine datasets, which are more politically and scientifically oriented.

  • Nature of Polarization: The nature of the “us vs. them” mentality also differs. In the Israel-Hamas context, it’s largely geopolitical. In the Ukraine theme, it’s tied to national interests versus international aid, sometimes it is tied to morality or friendship. Vaccine discussion is more about differing beliefs in science and authority.

  • Type of Action Advocated: The kind of action advocated varies, from military intervention (Israel-Hamas) to financial accountability and anti-corruption measures (Ukraine) to vaccine safety and informed consent (Vaccine).

Israel-Hamas Dataset Themes:

  • Conflict and Polarization: Focus on global unrest, power struggles, and a strong “us vs. them” narrative.

  • Distrust in Leadership and Institutions: Skepticism towards the US administration, global institutions, and leaders, coupled with a sense of abandonment.

  • Conspiracy and Misinformation: The prevalence of conspiracy theories and misinformation, especially regarding political decisions and global events.

  • Humanitarian Concern vs. Political Cynicism: Tension between the desire to assist in humanitarian crises and disillusionment with political forces.

  • National Identity and Patriotism: Discussions reflecting nationalism, patriotism, and feelings of betrayal due to international policies.

  • Religious and Ethical Aspects: The significant role of religious and ethical considerations in evaluating global and domestic issues.

  • Social Activism and Public Opinion: Active societal involvement in activism, both genuine and performative, strongly emphasizing direct engagement and accountability.

  • Injustice and Moral Outrage: Express moral outrage at perceived injustices and empathy towards marginalized groups.

  • Grievance Culture: A prevalent sense of grievance, promoting a country-first mentality and social divisions.

  • Global Uncertainty and Anxiety: An underlying anxiety about the future, manifested through references to global events.

Ukraine Dataset Themes:

  • Conspiracy Theories and ‘Good vs. Evil’: Narratives often delve into conspiracy theories and present the world in binary terms.

  • Injustice and Demand for Action: A strong sense of injustice and calls for urgent action to rectify perceived wrongs.

  • Financial Aid Distrust: Skepticism about financial aid to foreign countries, suggesting possible corruption or mismanagement.

  • Conspiracy and Self-Interest: Themes of conspiracy, hidden motives, and self-interest among leaders.

  • Hero vs. Villain Narrative: A clear demarcation between perceived “heroes” and “villains” in the public discourse.

  • Injustice and Urgency for Action: An urgent call for action to address perceived injustices.

  • Divisive Mentality: A strong “us vs. them” mentality within societal and political contexts.

  • International Consequences: Recognition of the global implications of financial and political decisions.

Vaccine Dataset Themes:

  • Vaccine Distrust and Authority Questioning: Deep mistrust in COVID-19 vaccines and questioning of the authorities involved in their development and distribution.

  • Conspiracy and Secrecy: The spread of conspiracy theories involving global authorities and vaccines.

  • Good vs. Evil Depiction: Framing the vaccine debate as a struggle between benevolent and malevolent forces.

  • Perceived Injustice and Calls for Action: A strong sense of injustice related to vaccines and calls for public action.

  • Divisive Thinking: Prominent “us vs. them” attitudes in vaccine narratives.

  • Global Impact and Variations: Acknowledgment of the global aspect of the vaccine debate and varying international approaches.

Statistical Significance

Statistical significance testing can help determine which findings are mere chance and which reflect a real difference.


The results paint a picture: authors who posted about all three issues — Israel-Hamas, Ukraine, and vaccines — had an average sentiment score that was distinct from the general data. Specifically, their posts were significantly more negative.

Wilcoxon Rank-Sum Test

The Wilcoxon rank-sum test helps us compare two separate groups to see if their scores tend to differ. It’s especially useful when you can’t assume the data follow a typical bell-shaped curve.

Here’s a breakdown of the test:

  • Null Hypothesis (H0): There’s no noteworthy difference in sentiment between the two groups.

  • Alternative Hypothesis (H1): One group’s sentiments are consistently different than the others’.

The p-value tells the likelihood of seeing the data in this report if the Null Hypothesis were true:

  • Small p-value (≤ 0.05): The difference is probably not due to chance.

  • Large p-value (> 0.05): Any difference might just be a coincidence.

The test gave us a p-value of approximately 0.00008912, confidently indicating a real difference in sentiment scores between the common authors and the general group.

Kruskal-Wallis Test

When there are more than two groups, you can use the Kruskal-Wallis test.

  • Data: Sentiment scores were compared across several groups.
  • Kruskal-Wallis chi-squared: The result was 33.437, indicating a noticeable difference between groups.
  • p-value: The p-value is exceptionally small (5.486e-08), suggesting a real difference.

The test suggests that the sentiment scores truly differ across groups — and that’s not something we’d expect to see by chance.

# R code for statistical tests
Warning: package 'readr' was built under R version 4.3.2
common_authors <- Reduce(intersect, list(unique(israel$author), unique(ukraine$author), unique(vaccine$author)))  

combined_data <- rbind(sentiment_israel, sentiment_ukraine, sentiment_vaccine) 
common_authors_data <- combined_data[combined_data$author %in% common_authors, ]  

sentiments_common <- common_authors_data$sentiment  
sentiments_general <- general$sentiment  

wilcox_result <- wilcox.test(sentiments_common, sentiments_general, paired = FALSE)  

    Wilcoxon signed rank test with continuity correction

data:  sentiments_common
V = 45603, p-value = 8.912e-05
alternative hypothesis: true location is not equal to 0
impression_general <- general$impression_count
impression_israel <- israel$impression_count

mwu_result_impression <- wilcox.test(impression_general, impression_israel)

    Wilcoxon rank sum test with continuity correction

data:  impression_general and impression_israel
W = 21150, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
like_general <- general$like_count
like_israel <- israel$like_count

mwu_result_like <- wilcox.test(like_general, like_israel)

    Wilcoxon rank sum test with continuity correction

data:  like_general and like_israel
W = 18200, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
sentiment_scores_israel <- data.frame(scores = sentiment_israel$sentiment, group = "israel")
sentiment_scores_ukraine <- data.frame(scores = sentiment_ukraine$sentiment, group = "ukraine")
sentiment_scores_vaccine <- data.frame(scores = sentiment_vaccine$sentiment, group = "vaccine")

combined_sentiment <- rbind(sentiment_scores_israel, sentiment_scores_ukraine, sentiment_scores_vaccine)
combined_sentiment$group <- as.factor(combined_sentiment$group)

kw_result <- kruskal.test(scores ~ group, data = combined_sentiment)

    Kruskal-Wallis rank sum test

data:  scores by group
Kruskal-Wallis chi-squared = 33.437, df = 2, p-value = 5.486e-08


No study is perfect. Here are some factors that might have nudged the results:

  • Keyword Selection: If the keyword search was missing popular keywords, that could affect the results.

  • Timing: When the data were collected matters, especially given the practice of ephemeral tweeting. Public opinion shifts over time, especially in response to real-world events. To accommodate for this, the window for tweets spanned two weeks.

  • Sample Representativeness: The “general” dataset is unlikely to reflect the broader population perfectly. It’s based on what was accessible, not a random sampling of all social media users.

  • Cultural Differences: Sentiments can be expressed differently across cultures, potentially skewing the interpretation of sentiment scores.

  • Language Nuances: Sarcasm, humor, and local slang are hard to interpret, even for humans, and might affect sentiment analysis.


Definitions and Terms

Author Groups

Categories of authors classified based on specific criteria related to their posting activity or the content they share. In this context, there are four groups: General, Single, Common-Two, and Common-All.

Common-Authors or Common_Authors_Data

Data containing the group of authors that appeared in all three datasets: Israel-Hamas, Ukraine, and Vaccine.

Data Visualization

A graphical representation that displays the sentiment scores across four distinct author groups. It combines violin plots and internal box plots to show the distribution and density of sentiment scores.

General (Group)

When capitalized this represents a specific dataset in this report. Data for this group was collected using the Israel-Hamas dataset keywords with no filter, meaning it collected all tweets that matched one keyword. The collection took place on October 7 and October 13. The tweets were collected without selection beyond the keywords. Tweets that received more than 20,000 likes (7 tweets) were removed from the dataset.


When referencing tweets, high performance means a tweet matched the Israel-Hamas keywords and received 20,000 likes or more, or it matched the keywords for Ukraine or vaccines and received 1,000 or more likes.


In the context of this data, it refers to the middle sentiment score in the distribution, with an equal number of scores more negative and more positive. A peak at the median in sentiment distribution suggests a high occurrence of neutral sentiments.

Most Relevant Voices

In the context of a crisis, InfoEpi Lab defines the most relevant voices as credible media, community leaders and citizens from the affected area, relevant experts, humanitarian workers, and elected officials or agencies involved in responding to the situation.

Sentiment Distribution

The spread of sentiment scores within the collected data, shows how frequently various sentiments, from negative to positive, are expressed in the posts.

Sentiment Scores

Numerical scores ranging from -1 to 1, assigned to posts to represent the sentiment expressed. A score of -1 indicates a highly negative sentiment, 0 is neutral, and 1 is highly positive.

In-One (Group)

A group of accounts that had at least one high-performance tweet in one category across the three datasets.


Authors that had high-performance tweets in more than one category.


Term for the group of authors that appeared in all three datasets: Israel-Hamas, Ukraine, and Vaccine.

Violin Plots

A method of data visualization that shows the distribution of data and its probability density. The width of the plot represents the frequency of sentiments, with a wider section indicating a higher occurrence of that particular sentiment score.


Keywords and Filters For Datasets
Table 7: Keywords for each dataset and the like threshold.
Dataset Keywords Filter
Israel-Hamas “israel,” “hamas,” “palestine,” “gaza” 20,000 likes
Ukraine “ukraine,” “ukrainians,” “mariupol,” “odesa,” “odessa,” “dpr,” “lnr,” “lpr,” “luhansk,” “lugansk,” “lviv,” “donbass,” “donbas,” “kharkiv,” “kherson,” “kiev,” “kyiv,” “crimea,” “chernobyl,” “right sector,” “ukronazi,” “sevastopol,” “rada,” “reznikov,” “zelenskyy,” “zelensky,” “zelenski,” “maidan,” “oblast,” “mykolaiv,” “azov,” “denazify,” “denazification” 1,000 likes
Vaccine “vaccine,” “vaccination,” “Pfizer,” “mRNA,” “diedsuddenly” 1,000 likes
Variables For Author Count Tables
Table 8: Definitions for variables in Author Count tables.
Variable Description
author The Twitter username of the author.
total_retweets The total number of retweets for the author’s tweets.
total_likes The total number of likes (favorites) for the author’s tweets.
total_quotes The total number of times the author’s tweets were quoted.
total_replies The total number of replies received on the author’s tweets.
total_engagements The total engagement count, sum of retweets, likes, quotes, replies.
total_impressions The total impressions of the author’s tweets, potential reach or views.
Packages required for this analysis

Account Engagement from Israel-Hamas Data

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Israel-Hamas Dataset: This dataset was collected from Twitter on October 20, 2023. The data start on October 7, 2023. Keywords for the search query: “israel,” “hamas,” “palestine,” and “gaza,” with an additional filter set to capture posts with a minimum of 20,000 likes.

Account Engagement from Ukraine Dataset

Ukraine Dataset: Keywords for the search query related to the Russian invasion of Ukraine, with a focus on Ukraine, “ukraine” OR “ukrainians” OR “mariupol” OR “odesa” OR “odessa” OR “dpr” OR “lnr” OR “lpr” OR “luhansk” OR “lugansk” OR “lviv” OR “donbass” OR “donbas” OR “kharkiv” OR “kherson” OR “kiev” OR “kyiv” OR “crimea” OR “chernobyl” OR “right sector” OR “ukronazi” OR “sevastopol” OR “rada” OR “reznikov” OR “zelenskyy” OR “zelensky” OR “zelenski” OR “maidan” OR “oblast” OR “mykolaiv” OR “azov” OR “denazify” OR “denazification” with an additional filter to limit posts to those with 1,000 likes or more.

Account Engagement from Vaccine Dataset

Vaccine Dataset: Keywords for this dataset included terms strongly associated with vaccine discussion online, “vaccine” OR “vaccination” OR “Pfizer” OR “mRNA” OR “diedsuddenly” with a filter set to exclude tweets that received fewer than 1,000 likes.


Analyzing the narratives from the provided texts revealed several themes and tropes that can be identified and structured uniformly across the three different datasets: Israel-Hamas, Ukraine, and Vaccine.

The narratives bear common themes of distrust, polarization, conspiracy theories, and calls for action. These stories reflect societal anxieties and deep-seated suspicions towards authorities and established systems, emphasizing a global struggle between perceived good and evil, truth and misinformation, and justice and corruption.

Israel-Hamas Dataset

Conflict and Polarization:

  • The messages heavily focus on conflicts involving Israel, Palestine, the broader Middle East, and Ukraine.

  • These stories draw parallels with domestic US issues, highlighting global unrest and instability. The Middle East and Ukraine situations are portrayed as global power struggles.

  • There’s a strong “us vs. them” narrative, with a clear division between perceived “good” and “evil” forces. This polarized view extends to domestic politics, framing issues in terms of stark opposition without much room for nuance or middle ground.

Distrust in Leadership and Institutions:

  • Much of the content expresses distrust in the US administration, global institutions, and leaders. They are often accused of incompetence, having ulterior motives, or being disconnected from the realities and needs of the general populace.

  • The narratives also suggest a perception of abandonment, where leaders prioritize international fame or diplomatic victories over their citizens’ immediate needs and security.

  • There’s also skepticism about the motives and actions of global institutions and other governments, with suggestions of hidden agendas and criticism of perceived inaction or counterproductive actions.

Conspiracy and Misinformation:

  • Several messages explore conspiracy theories, suggesting hidden agendas behind political decisions, especially involving foreign aid, diplomatic relations, and conflict resolutions.

  • These include accusations of undisclosed financial motives, secret alliances, and deliberate provocations of conflict. However, these interpretations and accusations lack evidence and rely on speculative or sensationalist claims.

  • Factual events are frequently distorted or presented without context, aiming to drive fear, confusion, and further polarization among the public. Hyperpartisan or misleading interpretations contribute to the idea of contested or manipulated “truth.”

Humanitarian Concern vs. Political Cynicism:

  • Many express concern over humanitarian crises like Gaza and the West Bank. However, there is cynicism about the political forces involved, with claims of misused or insufficient aid.

  • There is tension between the desire to help and disillusionment with entities responsible for providing aid.

National Identity and Patriotism:

  • Nationalism and patriotism are evident, especially in messages about US financial and military aid to other countries.

  • There is a sense of betrayal that the US is neglecting its citizens for international involvement.

  • Discussions about tax dollars reflect economic patriotism and a sense of ownership over national resources.

Religious and Ethical Aspects:

  • Religious language and imagery play a prominent role, especially in discussions about the Israeli-Palestinian conflict. This indicates that these issues are seen as more than political or territorial; they are considered moral and existential.

  • Ethical considerations are also present in discussions about leadership. Leaders are often evaluated based on their policies, perceived moral standing, and ethical behavior.

Social Activism and Public Opinion:

  • The narratives depict a society actively involved in both online and offline activism. There are references to protests, public opinion, and the influence of social media in shaping and amplifying these views.

  • In addition to genuine activism, there is a performative aspect, where support for various causes may be driven by trends, partisan groups, public pressure, or the desire for social validation rather than deep-rooted commitment.

  • Amidst the criticism and outrage, there are clear calls for action and accountability. These narratives encourage direct engagement, whether through protest, raising awareness, or holding leaders responsible. They reflect a desire for change and a proactive stance against current affairs.

Injustice and Moral Outrage:

  • Many messages express moral outrage at perceived injustices, especially in discussions about the Israeli-Palestinian conflict. Actions are condemned as genocidal, and financial decisions are viewed as corrupt or harmful to the public interest.

  • Empathy towards the underdog, victims, or marginalized groups is evident, along with a demand to acknowledge their struggles and condemn their oppressors.

Grievance Culture

  • A strong sense of grievance promotes a country-first mentality, especially regarding the US. This is evident in criticisms of foreign aid while domestic issues are neglected.

  • Items unrelated in spending (e.g. defense vs. social welfare programs) are falsely framed as competing with each other, leading to self-interest and ignoring the strategic or defensive benefits of certain decisions and policies.

  • Strong identification with specific groups or beliefs can foster an ‘us versus them’ mentality, creating social divisions based on differences rather than common goals or shared experiences.

Global Uncertainty and Anxiety:

  • Posts express an underlying anxiety about the future, repeatedly mentioning events like wars, resignations, and protests.

Ukraine Dataset

The Ukraine dataset narratives often explore conspiracy theories, depicting a world in stark ‘good vs. evil’ terms. They convey a strong sense of injustice and a demand for immediate action to address perceived wrongs. These stories express considerable skepticism about the handling of foreign aid by the government, suggesting corruption and conflicting national priorities.

These narratives also underscore a prevalent ‘us versus them’ mentality, indicating a divided world. Beyond merely identifying this split, the discussions acknowledge that the financial and political choices involved have global consequences. This points to a public that is actively engaged and worried about their nation’s international interactions and internal governance.

Financial Aid Distrust and Government Decisions:

  • There’s a common narrative of doubt and unhappiness concerning financial aid to foreign countries, especially Ukraine. These texts suggest these financial choices are poorly handled, with money not going to its intended uses or not serving the American people.

  • Stories focus on the significant amounts allocated to Ukraine, challenging the transparency and responsibility of these dealings. They imply that this financial assistance is mishandled, favoring corrupt officials, or diverted from urgent domestic matters in the US.

  • Some posts imply intentional mismanagement or corruption among leaders in the US and recipient nations, alleging they place foreign conflicts above domestic needs.

Conspiracy and Self-Interest:

  • Several narratives touch on conspiracy theories, hinting at organized attempts by political groups or elites to redirect public funds for personal profit or hidden motives under the pretense of foreign aid. There’s a repeated theme of concealed intentions behind the considerable aid, particularly to Ukraine, and an absence of tangible results from this spending.

  • This storyline expands to include public figures and organizations in these conspiracies, with individuals like Ukrainian President Zelenskyy often cited in connection with wasted or misused “taxpayer money.”

Hero vs. Villain Narrative:

  • Texts frequently present the situation as a battle between the honest, industrious public and the corrupt, self-interested political elite. Those questioning the aid or pointing out fund mismanagement are depicted as public allies, while those supporting or profiting from the aid are portrayed negatively.

  • There’s a story of enlightenment versus ignorance, where those challenging the financial choices are viewed as informed, while those who aren’t are seen as either involved or naively unaware of the ongoing misappropriation.

Injustice and Urgency for Action:

  • The narratives are threaded with a potent sense of injustice, with a sentiment that the misdirection of funds and focus on foreign aid significantly detracts from domestic concerns. Calls for reallocating resources to local issues, especially health, security, and economic matters, are common.

  • Many stories urge action, commonly through public protest, political engagement, or raising awareness about the suspected mismanagement of funds. These calls carry a sense of emergency and a moral obligation to “wake up” or hold leaders “accountable.”

Divisive Mentality:

  • The stories encourage a distinct “us vs. them” mindset, setting the public against political elites and policymakers. This conflict is depicted as a fight between communal welfare and the selfish desires of those in power.

  • This divide reaches into international politics, with some countries or groups shown as unfairly benefiting from misguided US priorities, often at the American people’s expense.

International Consequences and Differences:

  • The narratives recognize the global implications of these financial decisions, mentioning various countries and international scenarios. Different nations are shown as unjustly profiting from US aid or as participants in global conflicts that the US shouldn’t engage in.

  • The international backdrop is employed to emphasize perceived injustices or corrupt activities, indicating that these issues have extensive effects and influence international relations and leadership perception.

Vaccine Dataset

The vaccine-related tweets illustrate profound mistrust, anxiety, and suspected wrongdoing concerning COVID-19 vaccines and the parties overseeing their creation and distribution. The stories connect to larger issues of authority, transparency, and individual versus institutional roles in health decisions.

Vaccine Distrust and Authority Questioning:

  • Posts express serious doubt about the safety and effectiveness of COVID-19 vaccines, particularly mRNA vaccines. This doubt extends to the institutions endorsing them, like governments, health bodies, and pharmaceutical firms.

  • Narratives report severe adverse reactions and fatalities post-vaccination, stressing the perceived dangers of mRNA technology. They suggest that health issues, referred to as “VAIDS” in some texts, are common but intentionally ignored or hidden by those in power.

  • Some posts allege deliberate wrongdoing by vaccine producers, including concealing information about vaccine ingredients or potential health hazards.

Conspiracy and Secrecy:

  • Several stories entertain conspiracy theories, alluding to a coordinated effort by global authorities or groups to hide vaccine truths or use them for sinister ends. Recurring themes include hidden plans, such as vaccines having unknown components, ties to technology (like 5G), or even being instruments for population control.

  • The narrative accuses public figures and organizations of involvement in these conspiracies, with names like Bill Gates often mentioned. References to “predictive programming” and past events (like autism’s portrayal in media) suggest beliefs in long-term schemes by influential parties.

Good vs. Evil Depiction:

  • Some texts starkly frame the situation as a battle between benevolence and malevolence. Figures challenging or resisting vaccination initiatives, such as Robert Kennedy Jr. or certain officials, are seen as courageous truth-seekers or public defenders, while pro-vaccine individuals and groups are frequently portrayed as harmful or corrupt.

  • There’s a theme of martyrdom or persecution, where those questioning vaccine safety purportedly face suppression, mockery, or even fatal outcomes, as the story of a senator’s plane crash suggests.

Perceived Injustice and Calls for Action:

  • A robust sense of injustice pervades, with stories indicating individuals are harmed by vaccines but encounter resistance in seeking justice. This is apparent in stories about legal struggles, compensation efforts, and alleged corporate attempts to avoid responsibility.

  • Numerous texts call for action, through legal channels, public demonstrations, or informing others about the purported vaccine risks. These calls transmit a sense of urgency and ethical responsibility to “resist” or hold accountable those involved.

Divisive Thinking:

  • The narratives promote a pronounced “us vs. them” attitude, where vaccine skeptics and those questioning official COVID-19 responses confront a seemingly unified, distrustful, or malicious establishment.

  • This division extends beyond the public-establishment dichotomy to societal splits, highlighting partisan views on vaccination and descriptions of those with opposing opinions on vaccines or health measures.

Global Impact and Variations:

  • The global aspect of the vaccine debate is acknowledged, with references to various nations and international organizations.

  • Different countries are depicted as having diverse approaches to vaccine deployment, acceptance, and mandates, which are either criticized or applauded based on the narrative’s stance.


  • Updated October 25, 2023 to clarify wording and to correct a formatting error.


BibTeX citation:
  author = {InfoEpi Lab},
  publisher = {Information Epidemiology Lab},
  title = {Negative and {Misleading} {Posts} {Driving} {Critical}
    {Discussions} on {X}},
  journal = {InfoEpi Lab},
  date = {2023-10-24},
  url = {},
  doi = {10.7910/DVN/WRAQ4N},
  langid = {en}
For attribution, please cite this work as:
InfoEpi Lab. 2023. “Negative and Misleading Posts Driving Critical Discussions on X.” InfoEpi Lab, October.