# Successive cohorts of Twitter users
show increasing activity and shrinking content horizons

Frederik Wolf,1,2∗† Philipp Lorenz-Spreen,3∗† Sune Lehmann4,5†

1Potsdam Institute for Climate Impact Research (PIK)
\- Member of the Leibnitz Association, Telegrafenberg, 14473 Potsdam, Germany
2Department of Physics, Newtonstraße 15, Humboldt University, 12489 Berlin,
Germany

3Center for Adaptive Rationality,
Max Planck Institute for Human Development, Berlin, 14195, Germany

4Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Lyngby, DK-2800, Denmark

5Center for Social Data Science, University of Copenhagen, Copenhagen, 1353,
Denmark

∗These two authors contributed equally

† E-mails: Frederik Wolf –<EMAIL_ADDRESS>
Philipp Lorenz-Spreen –<EMAIL_ADDRESS>Sune Lehmann –
<EMAIL_ADDRESS>
> The global public sphere has changed dramatically over the past decades: A
> significant part of public discourse now takes place on algorithmically
> driven platforms owned by a handful of private companies. Despite its
> growing importance, there is scant large-scale academic research on the
> long-term evolution of user behaviour on these platforms, because the data
> are often proprietary to the platforms. Here, we evaluate the individual
> behaviour of 600,000 Twitter users between 2012 and 2019 and find empirical
> evidence for an acceleration of the way Twitter is used on an individual
> level. This manifests itself in the fact that cohorts of Twitter users
> behave differently depending on when they joined the platform. Behaviour
> within a cohort is relatively consistent over time and characterised by
> strong internal interactions, but over time behaviour from cohort to cohort
> shifts towards increased activity. Specifically, we measure this in terms of
> more tweets per user over time, denser interactions with others via
> retweets, and shorter content horizons, expressed as an individual’s
> decaying autocorrelation of topics over time. Our observations are explained
> by a growing proportion of active users who not only tweet more actively but
> also elicit more retweets. These behaviours suggest a collective
> contribution to an increased flow of information through each cohort’s news
> feed—an increase that potentially depletes available collective attention
> over time. Our findings complement recent, empirical work on social
> acceleration, which has been largely agnostic about individual user
> activity.

## Introduction

Year by year, the world is becoming more interconnected online (?), with news
(?), games (?), and entertainment (?) delivered to individuals via an
increasing number of smartphones and computers worldwide (?). The realization
that this development may not be unequivocally beneficial for individuals and
societies has spurred an active scientific and public debate (?, ?, ?, ?, ?,
?, ?), while positive consequences of the growing connectivity can also be
observed (?, ?, ?, ?). Still, most aspects of the complex interplay between
information technology, social interconnectedness, and human behaviour on the
collective (?), as well as on the individual level (?) have yet to be
empirically addressed through large-scale quantitative studies.

One crucial and overarching concept, which is discussed particularly in
sociology, is a development termed “social acceleration” (?). Social
acceleration is described as the interplay between the dimensions of
technological acceleration, acceleration of social change and the acceleration
of the pace of life (?). Our findings complement recent, empirical work on
social acceleration, which did not address individual user activity.

The dimension of technological acceleration has been quantified in a variety
of sectors, from genomic sequencing (?) to computing power (?) and
transmission of information (?). The other dimensions concerning the impact of
such technological developments on the social sphere are more difficult to
quantify empirically (?). But gradually more and more empirical hints for the
presence of social acceleration are emerging. Recent work has provided
evidence for acceleration of collective attention across various domains,
including information search, communication, and entertainment (?). These
findings are supported by other, empirical evidence for instances of social
acceleration, for example, on media consumption and production (?), the
editing style of Hollywood movies (?), the take-up of new concepts in books
(?), even the uptake of technological innovations itself (?) and, most
related, the information consumption on social media (?, ?, ?).

The majority of existing work, however, focuses on the aggregated, collective
level, leaving open the question of whether there is a general accelerating
trend on the individual (with a few exceptions, like surveys that point to a
reduction in sustained attention in reading behaviour (?)).

In other words, are the observed developments driven simply by the fact that
there are more people participating in consumption or discourse—and that they
became more visible there? Or do people behave differently now compared with
just a few years ago? Here we aim to address this question by illuminating and
quantifying one aspect of social acceleration, namely whether and how
individuals use Twitter differently over time.

We addressed these questions from the perspective of changing behaviour on
Twitter, using a longitudinal data set from which we sampled $600,000$
randomly chosen individual users with a total of $83,706,899$ retweet
interactions, within and outside that random sample. This data enabled us to
measure user behaviour over 8 years (2012–2019), spanning a large fraction of
the observable period of widespread social media adoption. Based on this data
set, we analyzed individual activity on Twitter from 2012 to 2019, aiming to
understand differences between users who joined the platform at different
points in time. We also explored other aspects of individual behaviour
—namely, the development of interactions with others and the amount of time
users pay attention to topics.

Our work connects to recent efforts to understand the long-term changes of
(and on) social media that have only now become quantitatively accessible,
despite the widespread use of social media over the last decade (?, ?).

Figure 1: User cohorts and user types in the Twitter data set. a) Illustration
of $1\%$ downsampling from the original tweet trajectory of a single user
between $2013$ and $2019$, which was necessary due to the reduced availability
through Twitter’s Decahose API. Illustration of the difference between user
cohorts (horizontal axes) and user types (vertical axes). User types are
sorted by their individual ratio of active to total days on Twitter. b)
Average tweets per week and user within each cohort, where the day of the
first tweet is aligned per cohort, showing an average Twitter career starting
at $t_{0}$ and lasting until 2019 for all users, due to our sampling strategy.
First years highlighted for comparison, subsequent years shaded.

## Results

A longitudinal dataset from Twitter’s Decahose API allowed us to pursue this
question (see Material and Methods section for details about the dataset). We
sample user who have been active at least twice, once in a specific month 2019
and once in the past after 2013 (see also the Material and Methods section for
details on the sampling strategy). In combination with personal identifiers,
this ensures that active Twitter users can be tracked over the entire period
and that the observed effects are not driven by people who had stopped using
the platform. To start, we analyze the activity of users in their first active
year (year after first recorded tweet or retweet) on Twitter. For a first
insight into the general trend we run a simple linear regression between the
individual starting date and the individual mean inter-event time (time
between own tweets or retweets of the first, active year). We found first
indications of individual acceleration: the inter-event time has decreased by
approximately $6.5$h per year between $2013$ and $2018$ (considering the
available $1\%$ sample).

For more detailed analyses, the heterogeneity of individual behaviour over
time make it necessary to pool users in order to create meaningful average
observational data at the systemic level. We choose two strategies, dividing
users into groups by the year they began using Twitter and by user type (Fig.
1a). Forming groups of users based on the year they were first active on
Twitter allows to divide them into eight cohorts that began using Twitter in
the different years (2012,2013,…2019). Dividing users into groups of
comparable user types according to their activity level makes it possible to
look beyond the simple cohort average and characterize the changing
composition of behaviours across cohorts.

### Users grouped by cohort

When dividing users into cohorts based on the year they were first active on
Twitter, we find that the tweeting behaviour of people who started using
Twitter in 2013 is substantially different from that of people who started
actively using Twitter more recently.

To assess the change in user activity over time, we first identify the date
$t_{0}$ of each user’s first recorded tweet, then split the users into cohorts
according to their starting year. To compare the behaviour of users who had
spent the same amount of time on Twitter, we align all users from each cohort
by setting their starting dates $t_{0}$ to an arbitrary but common date. This
makes it possible to approximately represent individual behaviour as an
average Twitter experience within a cohort and to evaluate possible
differences between cohorts. Fig. 1a illustrates this procedure.

We then calculate the mean number of tweets per week for all users at the same
stage of Twitter use, for each week of the observation period. Aligning
individual users creates an offset of the trajectories within each cohort,
which could be up to one year. To avoid this offset to extend beyond the
active period of a user from our sample, we ignore the year 2019 for each
cohort, leaving at least one year of buffer (and did not consider the 2019
cohort). Additionally, as we had no data from before 2012 and thus could not
know whether users had joined Twitter before our observation period, we ignore
the cohort from 2012. We interpret inactivity throughout 2012 as a proxy for
not having joined Twitter earlier.

Fig. 1b shows the resulting average tweeting activity per user for all cohorts
from 2013 to 2018—thereby going beyond merely illustrating the growth in the
number of users. This visualization highlights both the dissimilarity between
cohorts at the same stage of Twitter use and an offset in user activity
(measured as average tweets per week) after multiple cohorts. There is a clear
trend of increasing activity on the platform from one cohort to the next,
while activity levels remain stable over long periods within each cohort of
users (Fig. 1b).

While user behaviour changes between cohorts, it remains relatively stable
within cohorts. Users who were active on Twitter in 2013 are still using the
platform in a way that is similar to when they started. They also connect
preferentially to users who join around the same time. More generally, an
average of 90% of all retweets occur within a single cohort, despite
increasing total interactions, indicating homophily among contacts of the same
cohort. This finding mirrors previous findings of politically homophilous ties
in social media networks (?) and the formation of topical groups therein (?).

Users’ initial activity increases in each subsequent cohort and remains stable
(and increasing) at a higher level of activity, especially for the cohorts
after 2015. Furthermore, the cohorts end up on significantly different levels
of activity at the end of our observation period.

What drives this social acceleration, and what other dimensions of behaviour
changed?

### Users grouped by activity

Figure 2: Composition and inter-event time distributions in the first year of
user types. a) User types are based on clustering along with the ratio of
active days (at least one recorded tweet) versus total days on Twitter,
labelled from 1 (least active) to 8 (most active). Top: Median starting dates
(i.e., the first day of activity on Twitter $t_{0}$) for each user type.
Bottom: Box plot representation of the individual inter-event time
distribution for user types in seconds during their first year on Twitter. b)
Composition of user types per cohort.

To better understand the different roles users might play in this process, we
group users according to activity level. Our analysis indicates that the
observed development on the cohort level stems from a changing composition of
_user types_ , ranging from mostly passive spectators who tweet only
occasionally to extremely active users (Fig. 1a).

Activity was broadly distributed among users in each cohort (Fig. S1a, c, e).
To disentangle the heterogeneity of users, we compute the ratio of active days
(fraction of days with at least one recorded tweet) to total days (days
between the individual starting date $t_{0}$ and May 30, 2019) for each user
(Fig. 1a). We identify eight user types via a simple, unsupervised $k$-means-
clustering (using the Python package _scikit-learn_ (?)) and label them from 1
(least active) to 8 (most active). This method allows us to capture density
variations in the data and thus set our bin-edges in a data-driven way.

Figure 2a shows the resulting separation of the user types regarding their
activity. Note that, we here only consider the first year of activity (i.e.,
$t_{0}$) for each user to enable a meaningful comparison. Therefore, the upper
bound of the inter-event time equals $31536000$ seconds (one year).
Additionally, we emphasize that due to the 1% random sample of tweets, inter-
event times that we report cannot be easily interpreted as such but rather
serve as a proxy for individual activity.

Looking at user type and cohort, a trend becomes evident. Although we do not
consider starting dates in the clustering we used to define user types, we
find a striking one-to-one correspondence between activity and the median
starting date for individuals belonging to each user type. The more recent the
median starting date, the more active the user type (see Fig. 2a, top). In
particular, less active user types are only marginally separated in terms of
their median starting dates while there is a pronounced shift towards more
recent median starting dates for the more active user types. This points to a
change in individuals’ user types over time. To investigate this finding and
understand how user types are distributed within each cohort, we determine the
proportions of user types in each cohort.

Figure 2b illustrates how the composition of user types changed over time.
Perhaps the most striking observation is that the largest fractions of very
active users can be found in later cohorts. The histograms show the
proportions of user types in each cohort with respect to the absolute size of
the cohort. Note that there are substantial differences between user types
sizes (user type 1: $61,052$ users, user type 8: $445$ users; for all numbers
see SI Fig. 1d) and slight differences in cohort size. Therefore, absolute
numbers are not well represented in Fig. 2b as we show relative size increase.

To exclude the possibility that these observations are mainly driven by
automated accounts, beyond their relatively longevity in our sample, we
compare highly active users to a randomly chosen set of users. To measure
repetitive postings we use Shannon information (?, ?) to quantify the
complexity of the shared information. We find little difference in the
distribution of complexity of tweets (on the word level) between very active
user types and the random sample across all type (see Fig. S8a,b).

The evolving composition of user cohorts on Twitter highlights the fact that
the most active user types grew, relative to the size of their group, more
quickly than the less active user types did. Thus, the social acceleration
observed on the collective level is likely to be driven by people who joined
Twitter more recently and are using the platform differently compared to users
who have been on Twitter longer.

### Activity relates to network centrality

Figure 3: Properties of retweet interactions. a) In-degree ($k_{in}$)
distribution for each user type of the retweet interaction network,
corresponding to passive retweets (i.e., being retweeted by someone else). b)
Out-degree ($k_{out}$) distribution for each user type of the retweet
interaction network, corresponding to active retweets (i.e., retweeting
someone else). c) Illustration of the retweet network. Nodes represent users
and are colored by user type. Diameter of the nodes varies according to their
total degree; elevation along the z-axis shows activity (i.e., number of
original tweets). Visualization via gephi (?).

To unpack the observed behaviour of the increasingly large fractions of highly
active users further, next we focus on social interactions and how content
changes for individuals. We find that highly active users are also well
connected with others both actively (retweeting) and passively (being
retweeted).

To examine whether user types differed in respects other than their activity,
we constructed a weighted network (aggregated over the full time range of
$2012-2019$), consisting of both active and passive retweet interactions. For
the construction of the network, we included retweet interactions outside of
our random sample, by using each retweet from the full Twitter dataset
whenever either the active or the passive user was from our sample. The
network analyzed in Fig. 3, thereby consists of 648,880 unique users and a
total of 5,963,284 interactions, resulting in 1,516,958 unique, weighted
edges.

Figures 3a, b show the in- (Eq. 1) and out-degree (Eq. 2) distribution of the
eight user types. The increasing proportion of users with a high in- and out-
degree indicates that more active user types are more central on both
measures. Hence, more active users not only retweet more actively (which can
be expected due to their on average higher overall activity) but also elicit
more activity from other users. This is also reflected in the relatively high
reciprocity of the network (0.743). Reciprocity is the ratio of retweets that
were answered in the opposite direction at some point. High reciprocity
implies not only that activity increases over time (with more active users
joining) but also that interactivity among users becomes more frequent. In
other words, the observed trend towards higher activity does not appear to
occur in isolation but may be connected to a collective effect of mutual
social acceleration and denser interaction among Twitter users—for example,
when an elevated level of interaction leads to more content appearing in
users’ feeds. Yet individual activity can also speed up social acceleration:
by virtue of their high activity, users become more central, filling each
other’s feeds and thereby collectively contributing to the experienced social
acceleration.

### Content horizons

Furthermore, and possibly driven by that growing overall activity, the amount
of time any individual topic appears in people’s tweets is shrinking. We call
the amount of time that a topic tends to recur in a user’s tweets, that
person’s content horizon. We operationalize this notion as the autocorrelation
of hashtags an individual uses over time.

To set tweets in the context of an ongoing discussion, users employ hashtags,
a combination of the “#” symbol and keywords related to certain topics.
Additionally, users can include URLs in their posts to link content to their
activity. Did the way people interacted with content also change?

We find an increasing trend of sharing hashtags and URLs: Over our observation
period, the numbers of hashtags per tweet and URLs per tweet almost doubled.
While there is no clear difference among user types in terms of sharing URLs,
more active user types tend to use more hashtags per tweet compared to less
active user types (see Fig. S3). To understand the impact of the growing
amount of interactions and content at an individual level, we measure the
similarity of hashtags used over time and analyze an observable that we call
the content horizon, an adaptation of the broadly known concept of
autocorrelation.

To estimate the individual content horizons of Twitter users, we compute the
individual autocorrelation function $A_{i,j}(\tau)$ of hashtags used in tweets
(Eq. 3) as a proxy for characteristic length of time users focus on a specific
topic before moving on (see top panel of Fig. 4 for an illustration).

The decay of the autocorrelation is amplified in younger cohorts (Fig. 4).
This change indicates that users in younger cohorts stopped tweeting about
topics more quickly, potentially switching their focus to new topics. We call
this development, which becomes stronger in successive cohorts of Twitter
users, _shrinking content horizons_. Because we observe the qualitatively same
results for the different user types (see Fig. S4), we assume that the
shrinking content horizon is connected to rising activity on the part of
individual users.

Figure 4: Autocorrelation of hashtags. a) Illustration of the topical
autocorrelation $A_{i}(\tau)$, via the relative overlap of hashtags between a
shifted copy of one user’s tweeting trajectory. b) The resulting
autocorrelation as a function of an increasing time lag for the cohorts of
Twitter users. Values are excluding the point without time lag $\tau=0$ and
are relative to the maximum autocorrelation value for better comparison.

## Discussion

We found that the average activity per user on Twitter is increasing year over
year. We observe this as a cohort effect and that this change can be explained
by an effect of changing compositions of user types joining Twitter over time.
Changes in individual trajectories of Twitter use are more difficult to
measure due to the diversity of user type patterns on Twitter. Because not
only the sum of activities on Twitter is increasing (?), but also the activity
per user, the general development is likely to be due not only to a growing
user base, but to actual differences in individual behaviour. The change in
the composition of user types is accompanied by growing connectivity among
users through retweets, an increasing number of hashtags and URLs being
shared, and shrinking content horizons (here operationalized as quickly
decaying hashtag autocorrelations).

Even though we can not draw causal conclusions from our analysis, the
combination of observations allows us to offer an explanation of the factors
that are likely to contribute to an individual’s experience of social
acceleration on social media platforms: The growing fraction of highly active
users in each successive cohort combined with the pronounced tendency for
tweets to be retweeted within a single cohort leads to more interactions with
other users, most likely resulting in more and more active users filling up
their peers’ feeds with content. This development is accompanied by a growing
number of hashtags and URLs in tweets over time.

Although the amount of content on Twitter is increasing, the amount of
information on a Twitter feed that individual users can keep up with is
finite—a simple consequence of the amount of information that fits on the
screen in combination with users’ attentional capacities (?). Users,
therefore, typically encounter an overabundance of content (?). The inability
to keep up with everything that appears on their Twitter feed may explain
higher turnover rates of topics for each individual. We quantified this
development by measuring hashtag autocorrelation and confirmed the ever faster
decay of individual content horizons. This points to a possible behavioural
sequence of users in response to the growing information abundance: trading
breadth for depth in their information behaviour (?) The shift towards
spending less time discussing each topic, coupled with a simultaneous increase
in the number of topics discussed, may be experienced by users as an aspect of
social acceleration and is in line with previous results on the acceleration
on the collective level (?).

However, our data and methods come with several limitations. As the data only
includes active behaviour on Twitter it can not tell us much about the
mechanisms at play that go beyond our analysis of interactions and content.
For example, the data do not contain information about exposure (i.e., what
people saw in their surrounding before they tweeted) on and off Twitter, or
events outside of Twitter that drive activity exogenously (?). We aimed to
exclude the possibility of a predominant presence of automated accounts via a
complexity analysis of content from very active users, which does not seem to
be overly repetitive. But while many other unobserved factors are important to
better understand our observations, they do not trivialise our results; on the
contrary, exterior factors are important to complement our findings and fill
in the links between the dimensions that drive the broader development of
social acceleration, only a small instance of which we could quantify here.

For example, changes in the relative fraction of user types within each cohort
over time could be an indication that the platform itself is changing:
Platform design choices may be altering how Twitter users are motivated to
interact (?, ?, ?). Other mechanisms that go beyond our measurements can
include the increased professionalization and agenda-setting purposes of
social media usage (?) or an increasing migration of offline contacts to
social media. Furthermore, future research could aim to connect the observed
developments of acceleration, with attentional bottlenecks and the success of
the spread of negative, emotional or hostile content on social media (?, ?, ?,
?). To determine the drivers behind these potentially unintended developments
in public discourse—to distinguish between algorithmic curation, amplified
human tendencies, and societal developments—future research needs to examine
such developments, also on other platforms. This research is only possible
with access to data containing information about the platforms’ sorting
algorithms, design choices, exposure, as well as about observed behaviour (?).

Individuals are interacting with Twitter differently over time and influencing
each other in the process. Our work provides an empirical starting point
highlighting the need to quantify these complex, but important relationships
further. Ultimately, the ability to quantify the directions in which the
interplay of human behaviour, technological advancement and corporate
interests drive online behaviour and discourse would help society to actively
engage in shaping online discourse and identifying measures to promote a more
deliberate online experience (?, ?).

## Materials and Methods

### Data set

We use a data set from Twitter’s Decahose API. The data set features $10\%$ of
the Twitter traffic between January 1, 2012 and December 6, 2016 and $1\%$
between December 7, 2016 and June 6, 2019. To perform a continuous analysis
across the abrupt change in temporal resolution, we employ only $10\%$ of the
tweets in the first period, selected using random sampling (imitating the
sampling mechanism of the Twitter API). Consequently, we consider $1\%$ of all
tweets in the period between January 2012 and June 2019. A brief analysis of
the full $10\%$ sample leading to comparable results is shown in the
Supplementary Material, also reassuring that the different sampling methods do
not dramatically affect our results (?).

The metadata relates to each tweet consists of author ID, retweet ID,
timestamp in seconds, and full tweet text. As the tweets and users are
randomly sampled, there is no specific geographical or linguistic preference
in the data set.

For our analysis, we set up three user samples, each containing the data from
$200,000$ users. Users were selected randomly from all users who were active
(at least one reported tweet) in March ($200,000$ users out of $28,883,037$),
April ($200,000$ users out of $28,843,944$), and May 2019 ($200,000$ users out
of $29,353,930$). The user samples are almost distinct as the pairwise overlap
between the samples is less than 0.5%. Only two users appear in all three data
sets. This user selection allows us to track the individual user activity over
the whole time period (Jan 2012 - Jun 2019) by considering all tweets from
each selected user in the $1\%$ subset and it assures that there is no drop-
out of users in the studied period. Of course, this limits our analysis to
those users who have remained on the platform until Spring 2019. There are,
however, good reasons to believe that this selection does not strongly affect
our conclusion: users who dropped out may have behaved systematically
differently, but as these users are more likely to be from early cohorts, and
the assumption that they were less active on Twitter before they dropped out
would imply that differences in activity are possibly higher than reported in
our work.

In the main text, we show the results for the sample of users based on tweets
recorded in April 2019. We establish that our results are stable for other
user samples obtained in a distinct period in the Supplementary Material, by
showing results from analyzing the activity of users in the other two samples.
The choice of using the sample from April 2019 for the main manuscript is
arbitrary and aside from slightly different user type compositions, all
results from all samples agree quantitatively and qualitatively (see also the
Supplementary Material).

### Network analysis

We encode the topological information of a network with $N$ nodes (here
representing users) in the weighted adjacency $\boldsymbol{W}$. Matrix
elements $w_{ij}$ of $\boldsymbol{W}$ indicate how often user $i$ has
retweeted tweets from user $j$ (?, ?).

Hence, the in-degree is defined by (?)

$k^{i}_{in}=\sum_{i=1}^{N}w_{ji}.$ (1)

The out-degree is defined by (?)

$k^{i}_{out}=\sum_{i=1}^{N}w_{ij}.$ (2)

Active retweets are therefore represented by the out-degree (Eq. 2), and
passive retweets (e.g., if user $A$’s tweet was retweeted) are represented by
the in-degree (Eq. 1).

### Hashtag autocorrelation

We pursue a nonstandard approach and employ Jaccard similarity to enable
comparing the categorical hashtag data. Specifically, we define the lagged
correlation between two tweets at $t$ and $t+\tau$ of an individual user $i$
as

$A_{i}(\tau)=\frac{1}{N_{\text{match}}}\sum_{t=0}^{T-\tau}J(h_{i}(t),h_{i}(t+\tau)).$
(3)

Here, $T-\tau$ determines the number of weeks that the user has been active on
Twitter and $J(A,B)=\frac{A\cap B}{A\cup B}$ represents the Jaccard
similarity.

Zero correlations can be caused by different settings such as no activity, no
used hashtags, and no common hashtags. To avoid an activity bias in the
correlation measure, we exclude weeks in the computations during which one of
the users $i$ and $j$ had been inactive and normalize only by the number of
non-zero entries (i.e., $N_{\text{match}}$).

Supplementary Material: accompanies this paper at
http://www.scienceadvances.org/.

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Acknowledgements: We thank Deborah Ain for editing the manuscript and the
research group at the Center for Adaptive Rationality and Thomas Peron for
helpful comments and discussions. Funding: FW acknowledges financial support
from the International Research Training Group 1740/TRP 2011/50151-0 (funded
by the German Research Federation and the São Paulo State Foundation for the
Promotion of Research) and the German Ministry of Education and Research
(BMBF) via the project ClimXtreme (grant no. 01LP1902J). PLS acknowledges
financial support from the Volkswagen Foundation (grant “Reclaiming individual
autonomy and democratic discourse online: How to rebalance human and
algorithmic decision making”. SL acknowledges support from the HOPE project
(Carlsberg Foundation) and the Nation Scale Social Networks Project (Villum
Foundation).
Author Contributions: All authors designed the study. FW and PLS evaluated the
data. All authors analyzed the results and wrote the manuscript. FW and PLS
contributed equally to this work. Competing Interests: The authors declare
that they have no competing financial interests.