# Examining (Political) Content Consumption on Facebook Through Data Donation

Joao Couto1, Kiran Garimella1

###### Abstract

This paper investigates the usage patterns of Facebook among different
demographics in the United States, focusing on the consumption of political
information and its variability across age, gender, and ethnicity. Employing a
novel data donation model, we developed a tool that allows users to
voluntarily share their interactions with public Facebook groups and pages,
which we subsequently enrich using CrowdTangle. This approach enabled the
collection and analysis of a dataset comprising over 1,200 American users. Our
findings indicate that political content consumption on Facebook is relatively
low, averaging around 17%, and exhibits significant demographic variations.
Additionally, we provide insights into the temporal trends of these
interactions. The main contributions of this research include a methodological
framework for studying social media usage in a privacy-preserving manner, a
comprehensive dataset reflective of current engagement patterns, and
descriptive insights that highlight demographic disparities and trends over
time. This study enhances our understanding of social media’s role in
information dissemination and its implications for political engagement,
offering a valuable resource for researchers and policymakers in a landscape
where direct data access is diminishing.

##  1 Introduction

Understanding social media usage patterns is pivotal in the digital age, where
platforms such as Facebook significantly influence public opinion, cultural
norms, and political landscapes. Facebook, utilized by approximately 70% of
Americans, many of whom consume news and other information through this
medium, serves as a central hub for digital interactions (Gottfried 2023).
Despite its widespread use, comprehensive, empirical studies that unravel how
different demographics engage with content on Facebook are scarce. This
research aims to bridge this gap by exploring critical questions about
individual usage patterns, including the consumption of political information
and how these patterns vary across different age groups, genders, and
ethnicities, as well as their evolution over time.

The necessity to understand these patterns stems not only from Facebook’s
substantial user base but also from its role in disseminating information.
However, a significant challenge in studying Facebook usage is the restricted
access to user data, which is predominantly controlled by the platform itself.
This limitation poses a significant barrier for independent researchers who
seek to analyze the platform’s impact without access to internal data, as
traditional methods often rely on public data that is either incomplete or not
representative of broader user activities.

Historically, studies exploring social media usage were conducted in the
earlier phases of these networks’ development and did not capture data at the
granularity required today. Moreover, with growing concerns about
misinformation and data privacy, there is an increased need for updated
methodologies that respect user privacy while providing valuable insights.

To overcome these challenges, this paper introduces a novel approach utilizing
a data donation model, where users voluntarily share their interactions with
public Facebook groups and pages. This data is then enriched using
CrowdTangle, a tool that allows for an extended analysis of public posts and
interactions. Our methodology has enabled us to compile a comprehensive
dataset from over 1,200 American users, encompassing a wide range of age
groups, genders, and ethnic backgrounds. Our analysis reveals that political
content consumption on Facebook is relatively low, averaging around 17%
overall, which underscores the modest role that political engagement plays
within the broader spectrum of social media activity. This level of engagement
exhibits notable demographic variations: men are found to consume more
political content, while Hispanics and younger individuals aged 18-24 consume
the least, with the latter group’s consumption nearly half that of those in
the 65+ age bracket. Besides political content, our findings indicate that
users predominantly engage with lifestyle, entertainment, and religious
content, which collectively form a significant part of their social media
diet. Importantly, our data collection methodology provides a nuanced view of
Facebook usage, capturing intricate patterns of content consumption and
enabling detailed analyses over time. These insights are critical for
understanding the broader implications of social media on public discourse and
misinformation.

While our study provides extensive insights, it is important to note that our
results are descriptive and rely on data that represents only a segment of
Facebook’s total activity. Despite this, the implications of our research are
profound, offering a foundation for further studies in a post-API era where
direct access to platform data is increasingly restricted. Additionally, in
line with our commitment to transparency and furthering research in this
field, we will make our data and code available to the public, adhering to the
policies set by Facebook/CrowdTangle. This research not only advances our
understanding of social media dynamics but also equips researchers,
policymakers, and the public with the knowledge needed to navigate and
potentially regulate the digital landscape more effectively.

##  2 Background and Related Work

Data donation. Data donation, as explored by Ohme et al. (Ohme et al. 2023),
encompasses a variety of advanced data collection techniques that are crucial
for capturing and analyzing social media content effectively. These techniques
are designed to address the challenges of gathering raw social media data and
transforming it into meaningful metrics that reflect the nuanced impacts of
social media usage across different demographic and psychographic levels. This
approach allows for a detailed examination of how users interact with content
and how these interactions influence broader social and behavioral outcomes.

Keusch et al. (Keusch et al. 2023) further investigate the specific nuances of
data donation on Facebook, highlighting the factors that influence the success
or failure of these initiatives. Their findings emphasize the importance of
trust in the research team and distrust in the platform (Facebook) as
significant predictors of user participation in data donation schemes.
Interestingly, the manner in which data donation requests are framed appears
to have minimal impact on participation rates, suggesting that other factors
such as user engagement with the platform and their perceptions of privacy and
data security are more critical.

Due to the changes in access by various social media platforms, which
researchers term the dawn of the post-API age (Freelon 2018), the
implementation of data donation practices on a larger scale, such as in
projects by the National Internet Observatory, are increasingly becoming
common (Meyer et al. 2023). Despite its increasing popularity, the
quantitative exploration of data donation, especially on platforms like
Facebook, remains limited. This gap highlights the novelty of the current
study, which aims to operationalize data donation in innovative ways that
could set a precedent for future research in a landscape constrained by
limited access to traditional API-based data collection methods.

While data donation offers a promising avenue for obtaining high-quality,
consent-based data with a well-defined sampling frame, there are inherent
challenges and limitations to this approach. Conducting data donation at a
large scale presents logistical challenges, including ensuring sufficient user
participation and dealing with potential non-cooperation from social media
platforms, which may hinder data collection efforts. Additionally, the
significant upfront effort required by researchers may not always result in
successful data acquisition, illustrating the non-trivial nature of this
method compared to more traditional data collection techniques.

Nevertheless, the benefits of data donation are substantial. This method
ensures the collection of high-quality data that is both ethical, given its
opt-in nature and clear consent processes, and practical, allowing for the
integration of additional research tools such as surveys (De Vreese et al.
2017). The ability to capture public data without casting an excessively wide
net is particularly valuable for studies aimed at estimating prevalence and
understanding behaviors at a population scale. By building on the foundations
laid by previous studies and addressing the unique challenges of the post-API
era, this paper contributes to the ongoing dialogue on how best to harness the
power of user-generated content for scientific research.

Audit studies. In the evolving landscape of social media research, audit
studies represent a crucial methodology for understanding platform mechanisms
and their broader societal impacts. Here were explore the contributions and
methodologies of recent audit studies on various social platforms,
distinguishing how these approaches complement and diverge from our data
donation-based research.

A clear extension and application of data donations in auditing is the concept
of ‘end-user audits’, proposed by Lam et al. (Lam et al. 2022). These audits
involve typical platform users in the auditing process to perform certain
tasks which collectively can help in auditing a socio technical system. The
field of algorithmic auditing, although not the focus of our research,
provides valuable insights into the internal workings of social media
platforms. Metaxa et al. (Metaxa et al. 2021) highlight the significance of
understanding the algorithms that underpin user interactions and content
delivery on these platforms. While our study does not perform a direct
algorithm audit, the methodologies developed for such audits are informative
for our approach.

Audit studies on various platforms have been extensively documented. For
example, Robertson et al.(Robertson et al. 2018) audit Google search
algorithms to understand their operational biases and outcomes. Similarly,
audits on YouTube by Hosseinmardi et al.(Hosseinmardi et al. 2024) explore
biases in video recommendations, while Huszar et al.(Huszár, Ktena et al.
2022) focus on Twitter to analyze content dissemination practices. Beyond
social networks, platforms like Amazon and Uber have also been subjects of
audit studies, with works by Juneja et al.(Juneja and Mitra 2021) and Chen et
al. (Chen, Mislove, and Wilson 2015) examining marketplace biases and service
dynamics, respectively. These studies typically employ methodologies involving
artificial accounts (such as bots or sock puppets) or collaborate directly
with the platform to gather data.

Our current research, centered on data donation, does not directly engage in
such audits. However, the model and data collected through our methodology
could be readily adapted for auditing specific platform features, such as
group recommendations. The involvement of real users in our study provides a
unique opportunity to extend the research to include user surveys, which can
offer deeper insights into user perceptions and experiences, similar to those
gathered in end-user audits. By leveraging both donated data and direct user
feedback, our study could potentially bridge the gap between traditional audit
studies and innovative data donation approaches, offering a comprehensive view
of platform dynamics and user interactions.

Use of CrowdTangle. In examining the landscape of social media research,
particularly the consumption of news and political content on platforms like
Facebook, it becomes imperative to explore the methodologies and findings from
existing studies. This subsection delves into how Facebook data has been
utilized in research, the limitations of current data collection methods, and
the implications of these studies for understanding social media dynamics.

Since CrowdTangle, a tool that allows researchers to track interactions and
trends on Facebook (Tess 2018), became available to journalists, access to
Facebook data has significantly expanded. A search for “CrowdTangle” on Google
Scholar returns a substantial number of papers (2,740), underscoring its
widespread use in academic research. However, a major limitation of
CrowdTangle – and indeed, much of the data from Facebook – is the absence of
demographic details related to content consumption. This gap poses a
significant challenge for researchers aiming to understand how different
groups engage with content on the platform.

Studies on other platforms. The reliance on survey-based methods for
estimating social media usage is prevalent, with instruments such as those
deployed by Pew Research providing insights into these patterns (Gottfried
2023). However, such surveys often suffer from biases related to self-
reporting. Furthermore, existing studies provide a fragmented view of social
media engagement. For instance, Bestvater et al. (Bestvater, Shah, and Smith
2022) report that one-third of tweets from U.S. adults on Twitter are
political, with those aged 50 and older contributing to 78% of all political
tweets. This highlights the uneven distribution of content production across
different age groups.

Similarly, Allen et al. (Allen et al. 2020) explore news consumption across
various media, finding that only a maximum of 14% of Americans’ media diets
consist of news content. Moreover, television remains the dominant source of
news, overshadowing online sources by a substantial margin. This points to the
significant role traditional media still plays in news consumption, despite
the growing influence of digital platforms.

TODO: (Wojcieszak and Mutz 2009) show that around 17% of the people who were
online had some political discussion on discussion boards in 2009.

These findings are typically derived from large-scale nationally
representative surveys or data obtained from professional panel companies.
Both sources are prohibitively expensive for most researchers, which limits
the scope and frequency of such studies. This financial barrier underscores
the need for more accessible, cost-effective methods for gathering and
analyzing social media data, particularly as it relates to demographic
segmentation and content type.

##  3 Data Donation

To collect the data, we developed a Facebook application similar to popular
platforms like Farmville (Burroughs 2014), designed specifically for the
ethical collection of social media data through user consent. Users log in via
our website using their Facebook credentials, selectively granting access to
their interactions with groups and pages. This process leverages the Facebook
Graph API, particularly focusing on endpoints such as user_likes and
groups_access_member_info. Given the tightened security protocols post the
Cambridge Analytica scandal (Heawood 2018), our application underwent a
rigorous manual approval process to justify each permission request, ensuring
compliance with privacy standards. The design and flow of data collection
through our tool are illustrated in Figure 1. The technical backbone of the
application is built using Django, a high-level Python web framework that
efficiently handles user data interactions and API requests.

For each user, we get a list of Facebook groups and pages along with their
basic demographics such as age, gender and ethnicity. We used CrowdTangle to
obtain the posts shared in these pages/groups. Apart from the demographics and
the list of groups/pages for each user, we do not collect or store any other
personally identifying information about the users donating the data. Our data
collection received approval from the Institutional Review Board (IRB) at our
university.

Even though there is a vast amount of public information on Facebook,
including public pages and groups, accessing it presents significant
challenges due to the exclusive control platforms have over data concerning
the content consumed by users. Our data donation model allows us to bypass
these platform restrictions and gain deeper insights into user behavior by
allowing us access to get information on pages/groups from which users get
their information. This data donation approach also helps overcome limitations
seen in existing tools like CrowdTangle. While CrowdTangle provides some
access to Facebook data, it often fails in offering comprehensive coverage and
timely detection of critical content. This shortfall was notably evident
during the events leading up to significant incidents, such as the
coordination by “Stop the Steal” groups prior to the January 6th Capitol riot,
where relevant data was not promptly
accessible.111https://www.npr.org/2021/10/22/1048543513/facebook-groups-
jan-6-insurrection By focusing on public data and implementing an opt-in
model, our tool not only reduces the volume of data collected but also
significantly enhances our capacity to analyze public sentiment and detect
emerging trends. This methodology provides a crucial advantage in
understanding and responding to dynamics on social platforms in real-time.

Our method primarily captures potential content consumption based on user
interactions with public pages and groups, which, while not a direct measure
of news feed content, serves as a strong indicator of user interests and
preferences. This approach aligns with strategies used by researchers to infer
social media behavior in the absence of direct data access from platforms, as
discussed in studies such as those by Eady et al. (Eady et al. 2023). Despite
its limitations in capturing private group data and potential biases, this
methodology offers valuable insights into the public dimensions of platform
engagement, contributing to our understanding of digital communication
landscapes. With the implementation of GDPR and the forthcoming Digital
Services Act (DSA), there are already regulations in place that empower users
by mandating platforms to offer mechanisms for data access, such as the
ability for users to download their own data held by these platforms. This
provision enables researchers to utilize such data for analytical purposes.

Figure 1: Facebook data donation flowchart. Panel 1 – Welcome page where the
user sees a ‘Login with Facebook’ button. Panels 2–4 allow the users to select
what public Pages and Groups they wish to donate. Identifying details have
been anonymized.

We recruited users through an online survey panel company PureSpectrum.
Purespectrum provides high quality panels and have been used in other academic
studies (Lazer et al. 2020). The users donated their groups and took a 5
question survey asking them about demographics. The entire process takes less
than 3-5 minutes for the user. The entire data collection cost USD $2,500.

We obtained data from 1,261 users, which included a total of 251,220 pages and
groups – approximately 210,000 pages and 42,000 groups. Our objective was to
extract posts from these entities using CrowdTangle. However, due to
CrowdTangle’s limitation of 25,000 total entities per dashboard, we randomly
sampled 10% of both groups and pages, uploading them into separate lists on
separate dashboards.222Note: we use the term Pages for simplicity and not
Pages/Groups every time. A note on coverage by CrowdTangle: According to the
FAQ page (Tess 2024), any page that is not private is accessible via the
CrowdTangle API. Thus, our dataset covers all public pages irrespective of
their following. We miss a small fraction of pages (around 10%) which are
private. Navigating CrowdTangle’s official API to extract posts from our
sample, limited to six calls per minute per dashboard, was challenging. Each
call to the ‘/posts/’ endpoint returns a JSON containing 100 posts, their
metadata, and a pagination URL for the next batch of 100 posts. However, these
pagination chains are limited to 50,000 posts. Upon reaching this limit, our
extraction script executes a relocation query: it identifies the date of the
latest post received and initiates a new ‘/posts/’ query starting one second
earlier. From the resulting 100 posts, it locates the initial ‘latest’ post to
determine its offset within the result set. The new chain then begins with the
same relocation query, now including the offset as a parameter, ensuring no
posts are lost or duplicated.

The final dataset, comprises 32,026,862 posts published between October 2022
and September 2023.333The data collection is ongoing and will be extended
beyond this period, potentially until it CrowdTangle will be shut down in
August 2024. Due to the strict rate limits on CrowdTangle, we only present
analysis for this one year period in the current draft of the paper. Of those,
16,633,666 are tagged as English according to CrowdTangle’s ‘language_code’
field. It is noteworthy that the second largest language code in the dataset
is ‘und’ (undefined). Within the ‘und’ posts, there are many written in
English; however, this study only considers English posts.

The ethnic, gender and age distribution of our data is shown in Figures 2, 3.
We can see that we are oversampling women in most ethnicities and under
sampling users in the 18-24 age category. This is due to the convenient nature
of our sampling. To compensate for the lack of representative data, we use
reweighting techniques in our estimates in Section 5.

Figure 2: Fraction of users by ethnicity and gender. Figure 3: Fraction of
users by ethnicity and age.

##  4 Data Processing

In this section, we describe the various pre-processing steps taken before our
analysis. We first annotated a subset of the groups for high precision
political content.

###  4.1 Labeling of Political and Non-Political Content

To systematically investigate prevalence of political content in various
groups and pages, we started with extracting the top 1,000 unigrams and
bigrams present in the titles of the pages. Each of these n-grams was
carefully evaluated and classified as either politically relevant or not.
Political n-grams encompassed references to specific politicians (e.g.,
‘Trump,’ ‘Obama’) and broader political concepts (e.g., ‘Senate,’ ‘Congress’,
‘Republican’). Conversely, non-political n-grams were culled from a manually
curated list of the 1,000 most common unigrams and bigrams, including terms
such as ‘buy,’ ‘sell,’ ‘fan club,’ ‘real estate,’ ‘Taylor Swift,’ etc. While
not exhaustive, this list provided a comprehensive overview of non-political
discourse.

Following this classification, pages that featured any identified political
(non-political) n-gram in their names were designated as explicitly political
(non-political). This thorough review process yielded a dataset comprising
1,578 political and 3,678 non-political pages. Utilizing CrowdTangle, we then
collected all posts from these designated pages over the period from January
2021 through December 2023.

Subsequently, this ‘clean’ dataset served as the foundation for training a
classifier aimed at detecting political content. All posts obtained from
explicitly political pages were labeled as political, while those from non-
explicitly political pages were labeled as non-political.444Though this is a
strong assumption, we verified this manually by randomly sampling 250 posts
from each set and found a 97% of the posts from political pages were political
and 98% of the non political page posts were non political. The textual
content of these labeled posts was defined as the concatenation of the title,
description, and message fields obtained from the CrowdTangle API. Using this
labeled dataset, we aimed to create a classifier capable of distinguishing
political from non-political content in our larger unlabeled dataset.

###  4.2 Detection of Political Content

We built our political or not classifier by fine tuning an existing classifier
from (Gebhard and Hamborg 2020) on this clean political posts dataset from
Facebook. The classifier is trained on articles labeled in the “politics”
category in various news sources such as the BBC and Huffington Post. Even
though the base classifier already performs with a high F1 (94.4), we wanted
the classifier to be particularly good at detecting content from our Facebook
sample. We began by loading the model structure and weights provided by
(Gebhard and Hamborg 2020) and fine-tuned the model over 25 epochs, employing
an early stopping validation loss callback with a patience of three epochs to
prevent over fitting. The resulting fine-tuned model achieved an AUC-ROC of
0.951 on a previously unseen test set comprising 20% of all labeled posts.
Finally, the model was used to predict the probability each data point in our
unlabeled dataset contains political content.

Despite the fact that text posts constitute only a minor fraction of the
overall content (as illustrated in Figure 8), nearly all types of posts on
Facebook are accompanied by some form of text. Consequently, the coverage of
our political content classifier remains highly effective, covering
approximately 85% of the posts across various formats. This includes posts
where the primary content may be an image or video; the accompanying text
captions are utilized for classification purposes, ensuring broad
applicability of our analysis tools regardless of the post type.

###  4.3 Topics

To analyze the content in our unlabeled dataset of 16 million posts, we
utilized BERTopic for topic modeling (Grootendorst 2022). Given the large size
of our dataset, critical decisions regarding the model’s configuration were
necessary to ensure feasibility. The training process of a BERTopic model
involves four primary customizable submodules: embeddings, dimensionality
reduction, clustering, and tokenization. For embeddings, we used the sentence-
transformer’s ‘all-MiniLM-L6-v2’ model, as recommended by BERTopic defaults.
For dimensionality reduction, we employed UMAP with a ‘low_memory’ flag, which
trades high memory usage for a slightly more computationally intensive
k-nearest neighbors step. We used a count vectorizer limited to a n-gram range
of two for tokenization, instead of the typically recommended range of three,
as well as a min_df of two to prevent large sparse matrices for single-
occurrence words. Crucially, for the HDBSCAN clustering module, we performed a
comprehensive grid search to determine the optimal minimum cluster size, which
directly influenced the model’s computational feasibility and topic accuracy.

The BERTopic default parameters and FAQ suggest a minimum cluster size of 10
to 250 for large datasets. However, in our use case, this range was
insufficiently low and resulted in extremely high computational loads due to
the excessive number of topics generated. To address this, we sampled 20% of
our dataset and trained several dozen models using a grid search pattern with
varying minimum cluster sizes as percentages of our dataset. Initial
experiments with 1%, 0.1%, and 0.01% helped us narrow down the optimal range
for grid search to between 0.01% and 0.05%.

To quantify and compare the quality of these models we employed GenSim’s
coherence model (Rehurek, Sojka et al. 2011), which compares each topic’s
representation words with size one and two n-grams obtained from their
documents to give a final ‘coherence’ score. The higher this score the more
closely related the topics are to the documents labeled under them and the
better the model is. In our dataset the highest coherence score was obtained
with a minimum cluster size of 0.025% of the dataset sample used for topic
modelling. The resulting model identified 333 high-quality cohesive topic
clusters. After obtaining the topics, we manually annotated them and
identified the prevalence of various topics across various demographics.

###  4.4 Re-weighting to obtain population level estimates

In our study, we employed the ‘balance’ library from Facebook (Sarig, Galili,
and Eilat 2023) to address the challenge of biased sampling in our dataset.
This process involves generating propensity scores based on available
demographic information such as age, gender, and ethnicity. These scores are
then used to adjust the sample distribution to more closely resemble the
target population distribution. We overcome the convenience sampling biases by
generating weights for each data unit using various methods like inverse
propensity scoring, and evaluating the bias and variance post-adjustment. This
approach is crucial for our analysis as it allows us to derive population-
level statistics from data that may otherwise be skewed due to non-random
sampling processes. The findings in the subsequent sections are all weighted
and represent population level consumption estimates.

Note on reproducibility: The code for the data donation tool, the dataset
containing the demographics, names/IDs of pages/groups, keywords used for
identifying political/non-political pages, along with fine-tuned models for
detecting political content will be released upon paper acceptance.
Unfortunately CrowdTangle’s terms of service prohibit the release of the full
raw dataset we
collected,555https://help.crowdtangle.com/en/articles/3192685-citing-
crowdtangle-data but we hope the IDs and other artefacts might be valuable and
can be used on Facebook’s new open research and transparency tools for
academics: FORT.666https://fort.fb.com/

##  5 Political content

We divide our analysis into political content and overall content. First, in
this section, we show the prevalence of political content across various
demographic groups, look for trends over time and dig deeper into the
subcategories of political topics. In the next section, we get a look at the
overall content consumption by Facebook users.

###  5.1 Political content prevalence

This section examines the distribution and prevalence of political content
consumption among various demographic groups. Utilizing advanced statistical
techniques, we weighted all figures to provide population-level estimates,
enhancing the generalizability of our findings. In each plot, the dotted gray
line indicates the overall mean. The error bars indicate 95% confidence
intervals.

Overall, around 17% of the content being consumed is political content. These
findings align with broader media consumption trends identified in other
studies. A representative survey by Pew Research found that about a third of
tweets on Twitter were political (Bestvater, Shah, and Smith 2022), while a
large-scale study by Allen et al. reported an overall ‘news’ content
prevalence of about 14% across various platforms, suggesting that most
political content consumption still occurs through television (Allen et al.
2020). This comparative perspective highlights that while political engagement
on Facebook is less than on Twitter, it is comparable to traditional media
like television, suggesting that different platforms serve distinct roles in
political communication ecosystems.

Figure 4(a) reveals a differentiated pattern in the consumption of political
content among ethnic groups. Hispanics engage with political content
significantly less, at about 14%, compared to Asians and Whites, each around
20%. This disparity could be partly explained by the exclusion of Spanish
language content in our analysis, though not significantly, given that only 5%
of the content was in Spanish (while Hispanics made up 12% of our sample). As
depicted in Figure 4(b), age correlates strongly with political content
consumption. Younger individuals (18-24 years) show a markedly lower
engagement rate at 12%, in contrast to older users (65+ years), who engage at
almost a double that rate, of 24%. This finding is in line with previous
research which showed that older users are significantly more active in
consuming misinformation on Facebook (Guess, Nagler, and Tucker 2019). The
same pattern across age groups also holds for a ‘News’ content – as defined by
Facebook’s page/group categorization. See Figure 10 in the Appendix. Figure
4(c) indicates that men consume more political content than women. This study
provides the first detailed estimates of political content consumption on
Facebook, revealing that a majority of the content consumed by users is not
political. The granularity and quality of the data also allow us to slice
content further, say, among gender and ethnicity. For instance, Figure 4(d)
shows that men are more likely to consume political content across ethnicity.

These findings are crucial, particularly when considering the broader context
of political polarization and misinformation. They suggest that users,
especially young people, are not as inundated with political content as might
be feared. Instead, as detailed in Section 6, the predominant categories of
content consumed are related to religion, entertainment, and lifestyle. This
distribution indicates that while political content has a significant
presence, it does not dominate the social media landscape to the extent often
perceived in public discourse. Understanding these dynamics is essential for
addressing issues of political engagement, polarization and the spread of
misinformation effectively.

(a) Ethnicity

(b) Age group

(c) Gender

(d) Ethnicity and gender

Figure 4: Political content consumption by: (a) ethnicity, (b) age group, (c)
gender, (d) gender and ethnicity.

###  5.2 Political content consumption over time

Next, we go into further details of political consumption, looking at trends
in political consumption over time. Figure 5 shows the result. As we see,
there are significant periods of ebbs and flows of political content
corresponding to external events. Firstly, we see that certain patterns stay
consistent even over time – for instance, Hispanic and women and younger users
consistently have a significantly lower political consumption fraction.

To analyze the nature of posts contributing to peaks in political content, we
developed a separate BERTopic model. This model was trained exclusively on
posts with over 50% probability of containing political content, as assessed
by the fine-tuned neural network described in Section 4.2. The new model
identified 211 topic clusters, representing significantly more specific
political themes. For instance, while the BERTopic model in Section 4.3
grouped all US politics-related documents under a single topic, this new model
created distinct clusters for specific events, such as Donald Trump’s
indictment and the Joe Biden classified documents incident.

To elucidate the topical explanations for the peaks observed in Figure 5, we
identified the top five topics with the highest positive delta in their
prevalence each week, relative to the total number of posts in each
demographic segment. We then isolated the topics that appeared consistently in
the top five rankings within the same demographic category (e.g., male, female
within the gender category). Our analysis reveals distinct peaks in political
engagement that correlate with significant societal and political events.

Across all plots in Figure 5, we clearly see a drop off in political content
after the November 8th 2022 mid terms. Our data’s capacity to monitor content
consumption patterns at a high level of granularity can be illustrated via
some examples of peaks in the gender-bound timeseries in Figure 5 (a). We
observe notable peaks in political content during the weeks starting on
November 7th, February 20th, and June 19th. These peaks were driven by topics
consistently ranked among the top five rising topics for both men and women.
The peak on February 20th was entirely explained by a surge in discussions
about the Russia-Ukrainian war, corresponding with the one-year anniversary of
the Russian invasion of Ukraine.

In Figure 5 (b), three primary peaks of political content are observed on
March 20th, June 5th, and August 28th. During the week starting March 20th,
three of the top five rising political topics were common across various
ethnicity demographic segments: TikTok Ban (White +0.69%, Hispanic +0.42%,
Black +0.43%, Asian +1.24%), Donald Trump Indictment (White +1.05%, Black
+0.47%, Asian +0.64%), and LGBT Affirmative Action (White +0.5%, Black +0.5%).
Similarly, the June 5th peak saw four shared topics: Canadian Wildfires (White
+1.6%, Hispanic +0.8%, Black +1.0%, Asian +1.3%), TikTok Ban (White +0.47%,
Hispanic +0.78%, Black +0.2%, Asian +0.83%), Donald Trump Indictment (White
+1.16%, Hispanic +0.84%, Black +0.9%), and LGBT Affirmative Action (Hispanic
+1.22%, Black +1.41%). The peak on August 28th was driven by increased
prevalence of the following topics: Hurricane Ian (White +0.62%, Hispanic
+0.60%, Black +0.42%, Asian +0.30%) and Joe Biden Campaign (White +0.27%,
Black +0.42%).

(a) Gender

(b) Ethnicity

(c) Age group

Figure 5: Time series. The data spans from October 2022 to October 2023.

###  5.3 Political leanings of various demographics

Analyzing the political orientation of users through the bias of URLs shared
in posts is a viable approach, despite link content making up only 27% of the
overall content. While this sample may not be entirely representative, it
allows us to estimate user biases, following methodologies employed in
previous studies such as those by Allcott and Gentzkow (2017) and Grinberg et
al. (2019). However, this approach does not account for biases potentially
conveyed through images, videos, and other media formats. Initially, our goal
was to extend this analysis to such content types to classify political
support more comprehensively, but this proved to be more challenging than
anticipated so we instead opted to use the domain level leaning from Robertson
et al. (Robertson et al. 2018).

To visualize the distribution of these bias scores across the dataset, we
plotted the histogram of the URL bias distribution and estimated the density
using kernel density estimation (KDE) techniques. KDE helps in producing
smoother probability density curves, offering a clearer view of the data
distribution compared to traditional histograms.

Figure 6 illustrates the variation in URL bias across different demographics,
where a score of -1 indicates liberal domains and +1 indicates conservative
domains. Our findings reveal a predominantly centrist distribution with a
slight rightward skew (Figure 6 (a)). Women tend to lean slightly right,
whereas men generally appear centrist (Figure 6 (b)). Ethnic disparities were
also notable; Asian demographics mostly leaned left, while Hispanic groups
leaned slightly rightward (Figure 6 (c)). Age also played a critical role in
bias distribution: younger individuals tended to cluster around the center,
whereas older demographics displayed a bimodal distribution, with peaks on
both ends of the political spectrum.

Additionally, we investigated the prevalence of low-quality or ‘fake news’
URLs using sources listed by Media Bias Fact Check (Weld, Glenski, and Althoff
2021), finding them to constitute less than 0.01% of the total urls shared.
This minimal presence suggests effective moderation by Facebook of domains
known for low-quality content, aligning with the platform’s policies against
misinformation.

(a) Overall

(b) Gender

(c) Ethnicity

(d) Age groups

Figure 6: Political bias in URL consumption. Negative numbers indicate a
liberal bias.

##  6 Overall content consumption

The analyses in the previous section indicated that no more than 20% of the
content that people engage with is political in nature. This prompts an
investigation into the nature of the remaining 80% of content that captures
the majority of audience interest. To this end, we conducted a detailed
examination of content preferences across various demographic groups, focusing
specifically on the top 10 topics by volume for each demographic.

We manually coding the content topics for each demographic segment to
ascertain the prevalence and distribution of interest areas. The findings
reveal a compelling trend: the majority of demographic groups typically
feature only one or two political topics within their top 10, with many groups
showing none at all. However, there are notable exceptions. Individuals aged
18-24 showed a distinct interest in the Russia-Ukraine conflict, while
demographics identified as White, Black, men, and those aged 65 and older
exhibited a higher engagement with US politics. Additionally, the topic of
crime and shootings was predominantly favored among Black audiences.

The vast majority of content that dominates people’s consumption falls into
broader categories such as sports, lifestyle and culture, religion, and
entertainment. These categories represent a diverse range of interests that
reflect more everyday activities and personal preferences rather than
political engagement. Figure 7 provides a distribution of these top-level
categories across the top 10 topics for all demographics analyzed. This
visualization underscores the varied nature of content that resonates with
different audience segments, highlighting the substantial influence of non-
political topics in shaping public discourse and media consumption.

Figure 7: Top level aggregated categories. It is clear that in the top 10
categories across demographics, politics is rare.

###  6.1 Topics disproportionately consumed by specific demographics

In this section, we identify topics that are consumed disproportionately by
specific demographic groups, which showed statistically significant
differences compared to others. These differences illuminate the unique
preferences and informational needs that characterize diverse demographic
segments. Table 1 provides a summary of these differences, only including on
the youngest (18-24) and oldest (65+) for age groups. For instance, Figure 13
panels (a–c) (Appendix) exemplify the variations in topic interest across
demographics, revealing a statistically significant higher fraction of
engagement among male, Asian, and 65+ users for certain topics. The findings
provide interesting and several surprising insights, while also confirming
various stereotypes. For instance, white users were significantly more
interested in activities such as art, bird watching, beer brewing and
entertainment. Black users were significantly more interested in sports,
family and civil rights.

It is important to note that our reporting only includes topics where the
interest was statistically significant. We abstain from discussing topics
where the differences were not substantial, to keep the discussion clear. The
comprehensive data, including raw plots for all demographic categories, is
detailed in the Appendix for those interested in a deeper dive into the full
range of content consumption patterns observed.

This analysis not only helps in understanding the diverse content preferences
across demographic groups but also aids stakeholders in tailoring
communication strategies effectively. By understanding these preferences,
content creators and policymakers can better address the unique needs of
different demographic groups, enhancing engagement and information
dissemination.

Demographic |  Topics of Interest
---|---
White |  Art, Bird watching, Beer brewing, British royalty, Film entertainment, Parks, Social issues, US politics, Weather
Black |  Basketball, Wrestling, Film entertainment, Kardashians, Civil rights, College sports, Family
Asians |  Bollywood, Cricket
Hispanics |  Horoscope, British royalty (least)
Men |  Cars, Basketball, Crypto currencies, Gadgets
Women |  Bags and accessories, Jewelry, Skin care, Baking, Cooking, Home decor, Family, Horoscope, Kardashians, Animals
18-24 |  Cute babies, Horoscope, Friendship, Climate change
65+ |  Migrants on the border, Budlight boycott, Christianity, Lottery, Animals
Table 1: Interest Topics by Demographic.

###  6.2 Heterogeneity in content consumption across groups

This section delves into the varied modalities through which different
demographic groups consume information, highlighting significant differences
in content preferences that are not only relevant but also consequential for
studies related to information dissemination and misinformation.

We observed distinct patterns in the way content is consumed by different age
groups. As illustrated in Figure 8, older users, particularly those aged 65
and above, exhibit a pronounced preference for link-based content, consuming
nearly double the amount of such content compared to the 18-24 age group. In
contrast, younger users show a substantial inclination towards video content,
reflecting a dynamic shift in engagement as technology and media consumption
habits evolve.

The heterogeneity extends beyond age and into ethnic differences in content
consumption. Figure 9 reveals that White users are less likely to consume
video content, accounting for only 12% of their consumption, compared to
18-19% for other ethnic groups. Conversely, White users engage more frequently
with link-based content, at a rate 5-8% higher than that observed in other
demographics. Intriguingly, our analysis indicates no significant disparities
in content consumption patterns across genders.

These observed disparities are critical when considering the broader
implications for misinformation and its moderation. Previous research on
misinformation primarily focuses on the annotation of low-quality or ‘fake
news’ domains (Allcott and Gentzkow 2017; Grinberg et al. 2019). However, the
effectiveness of such interventions may vary significantly across different
demographic groups due to their divergent content consumption habits. For
instance, strategies that are effective in studying misinformation among older
adults may not be as successful with younger audiences who prefer different
content modalities.

Figure 8: Content types by age group. Figure 9: Content types by gender and
ethnicity.

##  7 Discussion

This paper introduces a pioneering data donation methodology aimed at
collecting and analyzing public Facebook preferences across a variety of
demographics. By employing reweighting techniques, we offer population-level
estimates, marking this as one of the inaugural studies estimating both
political and non-political content consumption on public Facebook pages. Our
findings highlight interesting insights, notably the prevalence of political
content and the demographic differences therein. Furthermore, our results
align with previous research that minimizes the scale and significance of
political content consumption online.

The significance of data donation as a platform-independent method is evident
through this work. However, a major challenge when working directly with
platforms is that researchers often find themselves at the mercy of these
platforms. Notably, we anticipate that the application used for this research
might be removed following the publication of this paper. While some of our
findings may appear ‘trivial,’ they underscore the utility of descriptive
analyses in unveiling nuanced consumption patterns. With this methodology,
researchers are empowered to uncover and understand diverse behavioral
insights that would otherwise remain obscured.

The generalizability of this data collection model is considerable. It can be
applied to various widely used platforms such as YouTube and Telegram, which,
despite having robust APIs, often offer data of substandard quality unless the
specific content relevant to the research is precisely identified. For
example, during the outbreak of the Russia-Ukraine conflict, it was commonly
known that Telegram was a significant source of information, yet researchers
had no means to determine which Telegram channels to monitor. A data donation
approach modeled on the one presented in this paper, where users could donate
just the list of public groups and channels they consume information from
could help pinpoint these critical sources of information.

As the utility of platforms like CrowdTangle wanes –a tool previously central
to social media research– questions about the continuing relevance of this
work emerge. Although this research is replicable with our current data, its
future extensions might be constrained, as discussed in recent studies
(Silverman 2024). Despite these challenges, data donation methodologies offer
a robust framework for future research endeavors. Moreover, even as tools like
CrowdTangle become obsolete, alternatives such as the Facebook Open Research
and Transparency (FORT) tools provide viable means for data acquisition,
accessible to academics.

Future work. The potential applications of our data donation methodology
extend far beyond the scope of this initial study. One promising direction for
future research involves leveraging this data to track how user preferences
evolve over time. With our methodology, once a user consents to data
collection, we can continuously monitor the pages and groups they follow until
they revoke this consent. This continuous data stream opens up the possibility
of studying how specific events, such as elections, influence users to engage
with certain types of content. Understanding these dynamics could provide
invaluable insights into the shifting landscape of public opinion and media
consumption.

Moreover, the data collected through this methodology could be used to develop
and train what we refer to as ‘artificial silicon samples’ (Argyle et al.
2023) or bots. These bots, based on real user behaviors, could simulate future
user interactions and preferences, providing a powerful tool for predictive
analytics and behavioral modeling in social media ecosystems (Hosseinmardi et
al. 2024).

Another avenue for future work involves designing targeted interventions. By
gaining a nuanced understanding of which demographics consume specific types
of content, we can craft personalized strategies aimed at enhancing
information literacy and countering misinformation. For example, if we
identify that a particular demographic predominantly consumes content that is
prone to misinformation, targeted educational content could be designed to
enhance critical thinking and fact-checking skills among that group.

While our current research has focused on textual content, the dataset we have
compiled is rich with other forms of media, including images and videos, which
we have yet to explore. Future studies could extend our analysis to these
mediums, potentially uncovering new patterns of engagement and preference that
are not visible through text-based analysis alone. Lastly, the topic of
misinformation, while touched upon briefly in this paper, warrants a deeper
investigation. Our methodology provides a unique opportunity to examine the
spread and impact of misinformation across different demographic groups.
Future research could utilize this data to develop more effective ways of
identifying and combating false information, thus contributing to healthier
public discourse.

Limitations. One of the significant limitations of the study is our focus on
English-language content. The development of a general-purpose political
classifier for content in languages other than English, particularly Spanish,
presented substantial challenges that we could not surmount within the scope
of this project. As a result, our findings may exhibit a certain degree of
bias, reflecting the content preferences and engagement patterns primarily of
English-speaking users. Efforts are underway to extend our methodology to
encompass additional languages, which will help mitigate this limitation in
future research.

Additionally, our analysis is constrained to public content on Facebook, which
does not necessarily encompass the entirety of content users encounter on
their feeds. Facebook has increasingly prioritized content from social
contacts over public pages and groups, and recent changes towards a TikTok-
like algorithmic feed further complicate this issue. This new model promotes
content from pages that users have not explicitly followed, potentially
skewing the visibility and engagement of public content. Consequently, the
data extracted from public pages and groups may not accurately represent the
full spectrum of information that users are exposed to in their daily
interactions on the platform.

Moreover, as Facebook continues to evolve its content delivery algorithms, the
gap between the content that users actually see and the content available for
analysis through public pages and groups may widen. This discrepancy
underscores the limitations inherent in our approach, as accessing
comprehensive user ‘diets’ from an external perspective remains a challenge.
Despite these constraints, our study provides a critical step toward
understanding content consumption dynamics on social media platforms, offering
a foundation for more nuanced analyses in the future.

Finally, while the data donation approach offers a robust methodology for
studying user preferences and behaviors on social media, it also presents
potential risks if misused. The voluntary nature of data donation could lead
to privacy breaches if the donated data is not handled securely. For instance,
without stringent protocols on what data is collected and anonymized,
malicious entities could potentially access or infer sensitive information
about individuals’ political inclinations, personal interests, or affiliations
from the collected data. Furthermore, there is a risk that the data, although
donated for research purposes, could be repurposed for commercial or political
campaigns without the explicit consent of the users (like what happened with
Cambridge Analytica). This not only violates ethical standards but also
undermines public trust in data donation initiatives. To mitigate these risks,
it is imperative to implement rigorous data management protocols that ensure
data anonymity and restrict access to authorized personnel only, thereby
preserving the integrity and ethical foundation of the research.

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## Appendix

Young people consume less “News”. these are according to categories for
pages/groups assigned by Facebook. Figure 10 shows the numbers. The numbers
closely reflect the estimates we have in Figure 4(b).

Figure 10: News consumption by age group.

Men consume so much more sports: Figure 11

Figure 11: Sports consumption by gender.

Asians consume significantly higher content from verified sources (compared to
Hispanics). Figure 12 Over 50% of the content Asians consume is from verified
pages, where as its close to 40% for Hispanics.

Figure 12: Verified content consumption by ethnicity.

(a)

(b)

(c)

Figure 13: (a) Topic: American Football proportion by gender, (b) Topic:
Bollywood proportion by ethnicity, (c) Topic: Budlight boycott proportion by
age group

Political content consumption by community within Asian American (unweighted).
This granular level of detail shows the value of our data donation model.
Figure 14

Figure 14: Political content consumption by community within Asian American.

American football is mostly consumed by older people. younger people do not
consume it. Figure 15. Same trend for Baseball, college sports,

Figure 15: American football by age group

American football (and basketball) consumption (similar to sports) is much
higher in men. Figure 16.

Figure 16: American football by gender

(a) Art

(b) Bird watching

(c) Beer brewing

(d) British royalty

Figure 17: Topic specific bar charts showing prevalence of various topics.

(a) Film entertainment

(b) Parks

(c) Social issues

(d) US Politics

Figure 18: Topic specific bar charts showing prevalence of various topics.

(a) Weather

(b) Basketball

(c) Civil rights

(d) Kardashians

Figure 19: Topic specific bar charts showing prevalence of various topics.

(a) Family

(b) Bollywood

(c) Cricket

(d) Horoscope

Figure 20: Topic specific bar charts showing prevalence of various topics.

(a) Cooking

(b) Crypto currencies

(c) Automotives

(d) Bags and accessories

Figure 21: Topic specific bar charts showing prevalence of various topics.

(a) Cats

(b) Cute babies

(c) Friendship

(d) Migrants on the border

Figure 22: Topic specific bar charts showing prevalence of various topics.