# Recommenders with a mission: assessing diversity in news recommendations

Sanne Vrijenhoek  Institute for Information Law, University of Amsterdam
<EMAIL_ADDRESS>,  Mesut Kaya  Independent Researcher1
<EMAIL_ADDRESS>,  Nadia Metoui  Department of Communication Science,
University of Amsterdam<EMAIL_ADDRESS>,  Judith Möller  Department of
Communication Science, University of Amsterdam<EMAIL_ADDRESS>,  Daan
Odijk  RTL Nederland B.V<EMAIL_ADDRESS>and  Natali Helberger  Institute
for Information Law, University of Amsterdam<EMAIL_ADDRESS>
###### Abstract.

News recommenders help users to find relevant online content and have the
potential to fulfill a crucial role in a democratic society, directing the
scarce attention of citizens towards the information that is most important to
them. Simultaneously, recent concerns about so-called filter bubbles,
misinformation and selective exposure are symptomatic of the disruptive
potential of these digital news recommenders. Recommender systems can make or
break filter bubbles, and as such can be instrumental in creating either a
more closed or a more open internet. Current approaches to evaluating
recommender systems are often focused on measuring an increase in user clicks
and short-term engagement, rather than measuring the user’s longer term
interest in diverse and important information.

This paper aims to bridge the gap between normative notions of diversity,
rooted in democratic theory, and quantitative metrics necessary for evaluating
the recommender system. We propose a set of metrics grounded in social science
interpretations of diversity and suggest ways for practical implementations.

1This work was done while the author was at TU Delft.

##  1\. Introduction

News recommender algorithms have the potential to fulfill a crucial role in
democratic society. By filtering and sorting information and news,
recommenders can help users to overcome maybe the greatest challenge of the
online information environment: finding and selecting relevant online content
- content they need to be informed citizens, be on top of relevant
developments, and have their say (Eskens et al., 2017). Informed by data on
what a user likes to read, what people similar to him or her like to read,
what content sells best, etc., recommenders use machine learning and AI
techniques to make ever smarter suggestions to their users (Thurman and
Schifferes, 2012; Dörr, 2016; Lewis and Westlund, 2015; Kunaver and Pozrl,
2017). For the news media, algorithmic recommendations offer a way to remain
relevant on the global competition for attention, create higher levels of
engagement with content, develop ways of informing citizens and offer services
that people are actually willing to pay for (Bodo, 2019). With this comes the
power to channel attention and shape individual reading agendas and thus new
risks and responsibilities. Recommender systems can be pivotal in deciding
what kind of news the public does and does not see. Depending on their design,
recommenders can either unlock the diversity of online information (Nguyen et
al., 2014; Helberger, 2019) for their users, or lock them into boring routines
of ”more of the same”, or in the most extreme case into so-called filter
bubbles (Pariser, 2011) and information sphericules.

The most frequently used key performance indicators, or KPIs, for optimizing
recommender systems, assess and aim to maximize short-term user engagement,
such as click-through rate or time spent on a page (Jannach and Jugovac,
2019). Often, these KPIs are defined by data limitations, and by technological
and business demands rather than the societal and democratic mission of the
media. More recently however a process of re-thinking algorithmic recommender
design has begun, in response to concerns from users (Thurman et al., 2019),
regulators (e.g., EU HLEG (on Artificial Intelligence, 2018)), academics, and
news organizations themselves (Bodo, 2019). Finding ways to develop new
metrics and models of more ”diverse” recommendations has developed into a
vibrant field of experimentation - in academia as well as in the data science
and R&D departments of a growing number of media corporations.

But what exactly does diverse mean, and how much diversity is ’enough’? As
central as diversity (or pluralism, a notion that is often used
interchangeably) is to many debates about the optimal design of news
recommenders, as unclear it is what diverse recommender design actually
entails. Designing for diversity in recommendation systems can be achieved
with a value by design approach. Value by design has been defined as ”a
theoretically grounded approach to the design of technology that accounts for
human values in a principled and comprehensive manner throughout the design
process,” or what Friedman et.al. called a ”pro-active design component”
(Friedman et al., 2017). A pro-active design component means finding ways to
conceptualize and implement a value (here diversity) in a way that can inform
the design of recommendation algorithms. This is distinct from research that
seeks to measure (empirically) the effects of news recommenders on the
diversity of contents that users are exposed to. Translating a value into
concrete design requirements is not a straightforward process. It can involve
going through several iterations of testing and adjusting the parameters or
models that inform an algorithm. In the growing literature that tries to
conceptualise and translate diversity into specific design requirements, a gap
between the computer science and the normative literature can be observed.
While diversity in the computer science literature is often defined as
concrete technical metrics, such as the intra-list distance of recommended
items (Vargas and Castells, 2011; Castells et al., 2015) (see also Section 2),
diversity in the normative sense is about grand concepts: democracy, freedom
of expressions, cultural inclusion, mutual respect and tolerance (Helberger,
2019). There is a mismatch between different theoretical understandings of the
construct of diversity, similar to the one observed in Fairness research
(Jacobs and Wallach, 2019). For news recommenders to be truly able to unlock
the abundance of information online and inform citizens better, it is
imperative to find ways to overcome the fundamental differences in approaching
diversity. There is a need to reconceptualise this central but also elusive
concept in a way that both does justice to the goals and values that diversity
must promote, as well as facilitates the translation of diversity into metrics
that are concrete enough to inform algorithmic design.

This paper describes the efforts of a team from computer science,
communication science, and media law and policy, to bridge this gap between
normative and computational approaches towards diversity, and translate
diversity, as a normative concept, to a concrete set of metrics that can be
used to evaluate and/or compare different news recommender designs.

We first survey diversity from a technical point of view (Section 2) and from
a social science interpretation, including its role in democratic models
(Section 3). In Section 4 we expand upon the social science notion of
diversity, and propose five metrics grounded in Information Retrieval that
reflect our normative approach. We cover the limitations of the proposed
metrics and this approach in Section 5. We conclude with detailing our
implementation of the metrics and the steps to undertake as a media company
when intending to adopt this normative notion of diversity in practice.

##  2\. A technical conception of diversity in news recommenders

Typically, generating a recommendation is seen as a reranking problem. Given a
set of candidate items, the goal is to present these items in such a way that
the user finds the item he or she is most interested in at the top, followed
by the second-most interesting one, and so on. How well this recommendation
reflects the actual interest of the user is called the _accuracy_ of the
recommendation. _Content-based_ approaches aim to maximize this accuracy by
looking at the type of items that the user has interacted with before and
recommend similar ones. In the context of news recommendations, one could
think of finding topics or overall texts that are similar to what is in the
user’s reading history. On the other hand, in _collaborative filtering_
approaches, the algorithm considers what other users similar to the user in
question have liked, and recommends those. Most state-of-the-art systems are
hybrids of these approaches. Evaluation of the system can be done in both an
online and offline fashion; offline often includes testing the system on a
piece of held-out data on its accuracy, whereas online evaluation monitors for
increases or decreases of user interactions and click-through rates following
the issued recommendations (Beel et al., 2013a).
However, this approach by its definition unduly promotes the items similar to
what a user has seen before, locking the user in a feedback loop of ”more of
the same” (McNee et al., 2006). It also introduces a so-called ”confounding
bias” (Chaney et al., 2018), which happens when an algorithm attempts to model
user behavior when the algorithm itself influences that behavior. To tackle
this in many currently operational systems ”beyond-accuracy” metrics
diversity, novelty, serendipity and coverage are introduced. _Diversity_
reflects how different the items within the recommendation set are from each
other. One intuitive usecase can be found in the context of ambiguous search
queries. A user searching for ”orange” should receive results about the color,
the fruit, the telecom company, the Dutch royal family, and the river in
Namibia, and not just about the one the system thinks he or she is most likely
to be interested in. The challenge then lies in how to define this difference
or distance. In the context of news recommendations many different approaches
exist, such as using a cosine similarity on a bag of words model or by
calculating the distance between the article’s topics (Ziegler et al., 2005).
The concepts of _novelty_ and _serendipity_ are strongly linked. _Novelty_
reflects the likeliness that the user has never seen this item before, whereas
_serendipity_ reflects whether a user was positively surprised by the item in
question. However, an item can be novel without being serendipitous (such as
the weather forecast), and an item may also be serendipitous without being
novel (such as an item that has been seen a long time ago, but becomes
relevant again in light of recent events). A common approach to improving
novelty and serendipity is by unlocking the ”long tail” content of the system,
while still optimizing for user accuracy. The long tail refers to the ”lesser
known” content in the system, that is less popular and therefore seen by less
users. By recommending less popular content the recommender systems increase
the chance that an item is actually novel to a user.
Lastly, _coverage_ reflects to what extent all the items available in the
system have been recommended to at least a certain number of users. This
metric is naturally strongly influenced by the novelty of the recommendations,
as increasing the visibility of lesser-seen items increases the overall
coverage of all items.

##  3\. A democrative conception of diversity in news recommenders

What becomes apparent from the overview in Section 2 is that although there
are various attempts to conceptualize evaluation metrics beyond accuracy in
the computer science literature, these metrics are constructed for the broad
field of recommendation systems, and are therefore not only relevant in the
context of news, but also for music, movies, web search queries and even
online dating. However, what they win in generalizability, they lose in
specificity. They are not grounded in, and do not refer back to the normative
understanding of diversity in the media law, fundamental rights law,
democratic theory and media studies/communication science literature.

Before we define more quantitative metrics to assess diversity in news
recommendation, we first offer a conceptualization of diversity. Following the
definition of the Council of Europe, diversity is not a goal in itself, it is
a concept with a mission, and it has a pivotal role in promoting the values
that define us as a democratic society. These values may differ according to
different democratic approaches. This article builds on a conceptualisation of
diversity in recommendations that have been developed by Helberger (2019).
Here, Helberger combines the normative understanding of diversity, meaning
what should diverse recommendations look like, with more empirical
conceptions, meaning what is the impact of diverse exposure on users. There
are many theories of democracy, but the paper by Helberger focuses on 4 of the
most commonly used theories when talking about the democratic role of the
media: _Liberal_ , _Participatory_ , _Deliberative_ and _Critical_ theories of
democracy (see also (Christians et al., 2009; Dahlberg, 2011; Karppinen,
[n.d.]; Strömbäck, 2005)).

It is important to note that no model is inherently better or worse than
another. Which model is followed is something that should be decided by the
media companies themselves, following their mission and dependent on the role
they want to play in a democratic society.

###  3.1. The Liberal model

In liberal democratic theory, individual freedom, including fundamental rights
such as the right to privacy and freedom of expression, dispersion of power
but also personal development and autonomy of citizens stands central. The
liberal model is in principal sympathetic to the idea of algorithmic
recommendations and considers recommenders as tools to enable citizens to
further their autonomy and find relevant content. The underlying premise is
that citizens know for themselves best what they need in terms of self-
fulfillment and exercising their fundamental rights to freedom of expression
and freedom to hold opinions, and even if they do not, this is only to a
limited extent a problem for democracy. This is because the normative
expectations of what it means to be a good citizen are comparatively low and
there is a strict division of tasks, in which _”political elites […] act,
whereas citizens react”_(Strömbäck, 2005).

Under such liberal perspective, diversity would entail a user-driven approach
to diversity that reflects citizens interests and preferences not only in
terms of content, but also in terms of for example style, language and
complexity. The _liberal recommender_ is required to inform citizens about
prominent issues, especially during key democratic moments such as election
time, but else it is expected to take little distance from personal
preferences. It is perfectly acceptable for citizens to be consuming primarily
cat videos and celebrity news, as long as doing so is an expression of their
autonomy.

#### Summary.

The liberal model of democracy promotes self-development and autonomous
decision making. As such, a news recommender following a liberal approach
should focus on the following criteria:

  * •

Facilitating the specialization of a user in an area of his/her choosing

  * •

Tailored to a user’s preferences, both in terms of content and in terms of
style

###  3.2. The Participatory model

An important difference between the liberal and the participatory model of
democracy is what it means to be a good citizen. Under participatory
conceptions, the role of (personal) freedom and autonomy is to further the
common good, rather than personal self-development (Held, 2006). Citizens
cannot afford to be uninterested in politics because they have an active role
to play in helping the community to thrive (Strömbäck, 2005). Accordingly, the
media, and by extension news recommenders must do more than to give citizens
’what they want’, and instead provide citizens with the information they need
to play their role as active and engaged citizens (Baker, 1998; Ferrer-Conill
and Jr., 2018; Karppinen, 2013; Jr. and Thomas, 2015), and to further the
participatory values, such as inclusiveness, equality, participation,
tolerance. _Participatory recommenders_ must also pro-actively address the
fear of missing out on important information and depth, and the concerns about
being left out. Here the challenge is to make a selection that gives a fair
representation of different ideas and opinions in society, while also helping
a user to gain a deeper understanding, and feeling engaged, rather than
confused. This also involves that recommenders are able to respond to the
different needs of users in which information is being presented. The form of
presentation is an aspect that is often neglected in discussions around news
recommender diversity, ignoring the fact that different people have different
preferences and cognitive abilities to process information. Accordingly, the
media should ’frame politics in a way that mobilizes people’s interests and
participation in politics’. Strömbäck (2005) and Ferree et al. (2002) speak of
’empowerment’: to be truly empowering, media content needs to be presented in
different forms and styles (Ferree et al., 2002; Christians, 2006; Zaller,
2003). By extension, this means that diversity is not only a matter of the
diversity of content, but also of communicative styles. What would then
characterize diversity in a participatory recommender are, on the one hand,
active editorial curation in the form of drawing attention to items that
citizens ’should know’, taking into account inclusive and proportional
representation of main political/ideological viewpoints in society; a focus on
political content/news, but also: non-news content that speaks to broader
public and, on the other hand, a heterogeneity of styles and tones, possibly
also emotional, empathetic, galvanizing, reconciliatory.

#### Summary.

The participatory model of democracy aims to enable people to play an active
role in society. It values the idea of the ‘common good’ over that of the
individual. Therefore, a participatory recommender should follow the following
principles:

  * •

Different users do not necessarily see the same articles, but they do see the
same topics.

  * •

Article’s complexity is tailored to a user’s preference and capability

  * •

Reflects the prevalent voices in society

  * •

Empathetic writing style

###  3.3. The Deliberative model

The participatory and the deliberative models of democracy have much in common
(compare Ferree et al. (2002)). Also in the deliberative or discursive
conceptions of democracy, community and active participation of virtuous
citizens stands central. One of the major differences is that the deliberative
model operates on the premise that ideas and preferences are not a given, but
that instead we must focus more on the process of identifying and negotiating
and, ultimately, agreeing on different values and issues (Ferree et al., 2002;
Karppinen, [n.d.]). Political and public will formation is not simply the
result of who has the most votes or ’buyers’, but it is the result of a
process of public scrutiny and intensive reflection (Held, 2006). This
involves a process of actively comparing and engaging with other also contrary
and opposing ideas (Manin, 1987). The epistemological shift from information
to deliberation has important implications for the way the role of news
recommenders can be conceptualised. Under a deliberative perspective, it is
not enough to ’simply’ inform people. The media need to do more, and has an
important role in ”promoting and indeed improving the quality of public life -
and not merely reporting on and complaining about it” (Christians et al.,
2009). Strömbäck (2005) goes even further and demands that the media should
also ”actively foster political discussions that are characterised by
impartiality, rationality, intellectual honesty and equality among the
participants”. Diversity in the deliberative conception has the important task
of confronting the audience with different and challenging viewpoints that
they did not consider before, or not in this way (Manin, 1987). Concretely,
this means that a deliberative recommender (or recommendation) should include
a higher share of articles presenting various perspectives, diversity of
emotions, range of different sources; it should strive for equal
representation, as well as on recommending items of balanced content,
commentary, discussion formats, background information; potentially some
prominence for public service media content (as the mission of many public
service media includes the creation of a deliberative public sphere), as well
as a preference for rational tone, consensus seeking, inviting commentary and
reflection.

#### Summary.

The focus of the deliberative recommender is on presenting different opinions
and values in society, with the goal of coming to a common consensus or
agreeing on different values.

  * •

Focus on topics that are currently at the center of public debate

  * •

Within those topics, present a plurality of voices and opinions

  * •

Impartial and rational writing style

###  3.4. The Critical model

A main thrust of criticism of the deliberative model is that it is too much
focused on rational choice, on drawing an artificial line between public and
private, on overvaluing agreement and disregarding the importance of conflict
and disagreement as a form of democratic exercise (Karppinen, 2013). The focus
on reason and tolerance muffles away the stark, sometimes shrill contrasts and
hidden inequalities that are present in society, or even discourage them from
developing their identity in the first place. Accordingly, under more radical
or critical perspectives, citizens should look beyond the paint of civil and
rational deliberation. They should discover and experience the many
marginalised voices of those ”who are ’outsiders within’ the system”(Ferree et
al., 2002), and when doing so critically reflect on reigning elites and their
ability to give these voices their rightful place in society. Diverse critical
recommenders hence do not simply give people what they want. Instead, they
actively nudge readers to experience otherness, and draw attention to the
marginalised, invisible or less powerful ideas and opinions in society. And
again, it is not only the question of what kinds of content are presented but
also the how: whereas in the deliberative and also the participatory model,
much focus is on a rational, reconciliary and measured tone, critical
recommenders would also offer room for alternative forms of presentations:
narratives that appeal to the ’normal’ citizen because they tell an everyday
life story, emotional and provocative content, even figurative and shrill
tones - all with the objective to escape the standard of civility and the
language of the stereotypical ”middle-aged, educated, blank white man”(Young,
1996).

#### Summary.

The critical recommender aims to provide a platform to those voices and
opinions that would otherwise go unheard. From a critical democracy
perspective on diversity, recommenders should be optimized on the following
principles:

  * •

Emphasis on voices from marginalized groups

  * •

Emotional writing style

##  4\. Diversity Metrics

The democratic models described in Section 3 lead to different
conceptualizations of diversity as a value, which again translate into
different diversity expectations for recommender systems. In this section, we
propose five metrics that follow directly from these expectations, grounded in
democratic theory and adapted from existing Information Retrieval metrics:
_Calibration_ , _Fragmentation_ , _Activation_ , _Representation_ and
_Alternative Voices_. For each of these metrics, we explain the concept and
link to democratic theory. Furthermore we make a suggestion for
operationalization, but note that this work is an initial outline and that
much work still needs to be done. Future work should include more work on the
validity of the metrics, for example by following the measurement models
specified in Jacobs and Wallach (2019). Lastly we mention a number of the
limitations of the currently proposed metrics and their operationalizations.

Table 1 provides an overview of the different models, metrics and their
expected value ranges. Note that not all metrics are relevant to all models.

Before explaining the metrics, we define the following variables that are
relevant to multiple metrics:

  * •

$p$: The list of articles the recommender system could make its selection
from, also referred to as the ’pool’

  * •

$q$: The unordered list of articles in the recommendation set

  * •

$Q$: The ordered list of articles in the recommendation set

  * •

$r$: The list of articles in a user’s reading history

###  4.1. Calibration

The _Calibration_ metric reflects to what extent the issued recommendations
reflect the user’s preferences. A score of 0 indicates a perfect Calibration,
whereas a higher score indicates a larger divergence from the user’s
preferences.

####  4.1.1. Explanation.

Calibration is a well-known metric in traditional recommender system
literature (Steck, 2018). It is calculated by measuring the difference in
distributions of categorical information, such as topics in the news domain or
genres in the movie domain, between what is currently recommended to the user
and what the user has consumed in the past. However, we extend our notion of
calibration beyond topicality or genre. News recommendations can also be
tailored to the user in terms of article style and complexity, allowing the
reader to receive content that is attuned to their information needs and
processing preferences. This may be split up within different topics; a user
may be an expert in the field of politics but less so in the field of
medicine, and may want to receive more complex articles in case of the first,
and less in case of the second.

####  4.1.2. In the context of democratic recommenders.

The Calibration metric is most significant for recommenders following the
Liberal and Participatory model. The aim of the Liberal model is to facilitate
user specialization, and assumes that the user eventually knows best what they
want to read. In these models, we expect the Calibration scores to be closer
to 0. On the other hand, the Participatory model favors the common good over
the individual. We therefore expect a higher degree of divergence in
Calibration, at least when considered in light of topicality. Both models, but
especially the Participatory model, require that the user receives content
that is tailored to their needs in terms of article complexity, and in this
context we expect a Calibration score that is closer to zero.

####  4.1.3. Operationalization.

For the operationalization of a recommender’s Calibration score it is
important to have information on not only an article’s topic and complexity,
which can potentially be automatically extracted from an article’s body (see
for example Feng et al. (2010) and Kim and Oh (2011)), but also on the user’s
preferences regarding this matter. Note that topicality can be both generic
(politics, entertainment, sports, etc) and more specific (climate change,
Arsenal). In light of democratic theory more fine-grained information is
preferable, but this is not always available. Steck (2018) uses the Kullback-
Leibler divergence between two probability distributions as Calibration
metric, as follows:

$Calibration_{(r,q)}=\sum_{c}r(c|u)log\frac{r(c|u)}{\tilde{q}(c|u)}$

where $r(c|u)$ is the distribution of categorical information $c$ across the
articles consumed by the user in the past, and $\tilde{q}(c|u)$ is an
approximation of $q(c|u)$ (necessary since KL divergence diverges if
$q(c|u)=0$), which is the distribution of the categories c across the current
recommendation set. As mentioned before, a score of 0 indicates that there is
no divergence between the two distributions, meaning they are identical. The
higher the Calibration score, the larger the divergence. As KL divergence can
yield very high scores when dividing by numbers close to zero, outliers can
greatly influence the average outcome. Therefore, the aggregate Calibration
score is calculated by taking the median of all the Calibration scores for
individual users.

####  4.1.4. Limitations.

This approach is tailored to categorical data, but sometimes our data may be
numerical rather than categorical, for example in the case of article
complexity. In these cases, a simple distance measure may suffice over the
more complex Kullback-Leibler divergence.

###  4.2. Fragmentation

The _Fragmentation_ metric denotes the amount of overlap between news story
chains shown to different users. A Fragmentation score of 0 indicates a
perfect overlap between users, whereas a score of 1 indicates no overlap at
all.

####  4.2.1. Explanation.

News recommender systems create a recommendation by filtering from a large
pool of available news items. By doing so they may stimulate a common public
sphere, or create smaller and more specialized ’bubbles’. This may occur both
in terms of topics recommended, which is the focus of the Fragmentation
metric, and in terms of presented perspectives, which will be later explained
in the Representation metric. Fragmentation specifically compares differences
in recommended news story chains, or sets of articles describing the same
issue or event from different perspectives, writing styles or points in time
(Nicholls and Bright, 2019), _between_ users; the smaller the difference, the
more aware the users are of the same events and issues in society, and the
more we can speak of a joint agenda. When the news story chains shown to the
users differ significantly, the public sphere becomes more fragmented, hence
the term Fragmentation.

####  4.2.2. In the context of democratic recommenders.

Both the Participatory and Deliberative models favor a common public sphere,
and therefore a Fragmentation score that is closer to zero. The Liberal model
on the other hand promotes the specialization of the user in their area of
interest, which in turn causes a higher Fragmentation score. Finally the
Critical model, with its emphasis on drawing attention to power imbalances
prevalent in society as a whole, calls for a low Fragmentation score.

####  4.2.3. Operationalization.

This metric requires that individual articles can be aggregated into higher-
level news story chains over time. This can be done through manual annotation
or automated extraction process. One unsupervised learning approach for doing
this automatically can be found in Nicholls and Bright (2019). Here the
articles are clustered in a similarity network based on tfidf or BM25F
distance between articles, with a moving three-day window for scalability.
Once the stories are identified, the Fragmentation score can be defined as the
aggregate average distance between all sets of recommendations between all
users. Dillahunt et al. (2015), which aimed to detect filter bubbles in search
engine results, defines this distance with the Kendall Tau Rank Distance
(KDT), which measures the number of pairwise disagreements between two lists
of ranked items. However, Kendall Tau is not suitable when the two lists can
be (largely) disjointed. It also penalizes differences at the top of the list
equally to those more at the bottom. Instead we base our approach on the Rank
Biased Overlap used in Webber et al. (2010):

$RBO(Q_{1},Q_{2},s)=(1-s)\sum_{d=1}^{\infty}s^{d-1}\cdot A_{d}$

where $Q_{1}$ and $Q_{2}$ denote two (potentially) infinite ordered lists, or
two recommendations issued to users 1 and 2, and $s$ a parameter that
generates a set of weights with a geometric progression starting at 1 and
moving towards 0 that ensures the tail of the recommendation is counted less
severely compared to its head. Because of this there is a natural cut-off
point where the score stabilizes. We iterate over the ranks $d$ in the
recommendation set, and at each rank we calculate the average overlap $A_{d}$.
Because Rank-Biased Overlap yields a score between 0 and 1, with 0 indicating
two completely disjoint lists and 1 a perfect overlap, and the score that is
expressed is semantically opposite of what we aim to express with the
Fragmentation metric, we obtain the Fragmentation score by calculating 1 minus
the Rank-Biased Overlap. Lastly, the aggregate Fragmentation score is
calculated by averaging the Fragmentation score between each user and every
other user.

####  4.2.4. Limitations.

Since this approach is computationally expensive (every user is compared to
every other user, which is $O(n^{2})$ complexity), some additional work is
needed on its scalability in practice, for example through sampling methods.

###  4.3. Activation

The _Activation_ metric expresses whether the issued recommendations are aimed
at inspiring the users to take action. A score close to 1 indicates a high
amount of activating content, whereas a score close to 0 indicates more
neutral content.

####  4.3.1. Explanation.

The way in which an article is written may affect the reader in some way. An
impartial article may foster understanding for different perspectives, whereas
an emotional article may activate them to undertake action. A lot of work has
been done on the effect of emotions and affect on the undertaking of
collective group action. This holds especially for anger, in combination with
a sense of group efficacy (Van Zomeren et al., 2004). But positive emotions
play a role too; for example, ”joy” elicits the urge to get involved, and
”hope” to dream big (Fredrickson, 2013). The link between emotions, affect and
activation is described well by Papacharissi (2016): _”…for it is affect that
provides the intensity with which we experience emotions like pain, joy, and
love, and more important, the urgency to act upon those feelings”_. The
Activation metric aims to capture this by measuring the strength of emotions
expressed in an article.

####  4.3.2. In the context of democratic recommenders.

The Activation metric is relevant in three of the four different models. The
Deliberative model aims for a common consensus and debate, and therefore would
give a certain measure of prominence to impartial articles with low Activation
scores. The Participatory model fosters the common good and understanding, and
aims to facilitate users in fulfilling their roles as citizens, undertaking
action when necessary. This leads to a slightly wider value range; some
activating content is desirable, but nothing too extreme. The Critical model
however focuses specifically on emotional and provocative content to challenge
the status quo. Here very high values of Activation should be expected.

####  4.3.3. Operationalization.

The Circumplex Model of Affect (Russell, 2003) describes a dimensional model
where all types of emotions are expressed using the terms _valence_ and
_arousal_. _Valence_ indicates whether the emotion is positive or negative,
while _arousal_ refers to the strength of the emotion and to what extent it
expresses action. Following this, for example, ’excitement’ has a positive
valence and arousal, whereas ’bored’ is negative for both. Based on the theory
described above a number of ”sentiment analysis” tools have been developed,
which typically have the goal of identifying whether people have a positive or
negative sentiment regarding a certain product or issue. For example, Hutto
and Gilbert (2014) provides a lexicon-based tool that for each input piece of
text outputs a compound score ranging from -1 (very negative) to 1 (very
positive). The absolute values of these scores can be used as an approximation
of the arousal and therefore be used to determine the Activation score of a
single article. Then, the total Activation score of the recommender system
should be calculated two-fold. The average Activation score of the items
recommended to each user provides a baseline score for whether the articles
overall tend to be activating or neutral. Next, the issued recommendations are
compared to the available pool of data as follows:

$Activation(p,q)=(|polarity(q)|-|polarity(p)|)/2$

Here $p$ denotes the set of all available articles in the pool, and $q$ those
in the recommendation. For both sets we take the mean of the absolute polarity
value of each article, which we use as an approximation for Activation. We
subtract the mean from the available pool of articles from the mean of the
recommendation set, which maps to a range of $[-1,1]$. A value lower than zero
indicates that the recommender system shows less activating content than was
available in the pool of data, and therefore favors more neutral articles.
Values higher than zero show the opposite; the recommendation sets contained
proportionally more activating content than was available in the pool.

####  4.3.4. Limitations.

Of principle importance is the impact that the article’s text has on the
reader. However, as we have no direct way of measuring this, we hold to the
assumption that a strongly emotional article will also cause similarly strong
emotions in a reader, which again translates into higher willingness to act.
It must also be noted that people may respond differently to different
emotions (for example, anger may incite either approach (action) or avoidance
(inaction) tendencies) (Schuck and Feinholdt, 2015). We therefore see this
approach as an approximation of the concept of activation, affect and emotion
in articles, until such a time when more research in the topic allows us to be
more nuanced in our perceptions.

###  4.4. Representation

The _Representation_ metric expresses whether the issued recommendations
provide a balance of different opinions and perspectives, where one is not
unduly more or less represented than others. A score close to zero indicates a
balance, where the model of democracy that is chosen determines what this
balance entails, whereas a higher score indicates larger discrepancies.

####  4.4.1. Explanation.

Representation is one of the more intuitive interpretations of diversity.
Depending on which model of democracy is chosen, news recommendations should
contain a plurality of different opinions. Here we care more about _what_ is
being said than _who_ says it, which is the goal of the final metric,
Alternative Voices. In order to define what it means to provide a balance of
opinions, one needs to refer back to the different models and their goals.

####  4.4.2. In the context of democratic recommenders.

The Participatory model aims to be _reflective_ of ”the real political world”.
Power relations that are therefore present in society should also be present
in the news recommendations, with a larger share in the Representation for the
more prevalent opinions. On the other hand, the Deliberative model aims to
provide an _equal_ overview of all opinions without one being more prevalent
than the other. One should keep in mind that not each recommendation by itself
needs to provide an overview of the full spectrum, but rather that the
aggregate of all recommendations should be complete and unbiased. The Critical
model has a large focus on shifting power balances, and it does so by giving a
platform to underrepresented opinions, thereby promoting an _inverse_ point of
view. In doing this, the Critical model also strongly considers the
characteristics of the opinion holder, specifically whether they are part of a
minority group or not, though this is the goal of the last metric, Altenrative
Voices.

####  4.4.3. Operationalization.

Representation, and Alternative Voices as well, rely strongly on the correct
and complete identification of the opinions and opinion holders mentioned in
the news. Though there is research available on the usage of Natural Language
patterns to extract opinion data from an article’s text (Pareti et al., 2013),
additional work is necessary on its applicability in this context. For
example, it is of significant importance that not one type of opinion or
opinion holder is systematically missed. Once the quality of the extraction is
relatively certain, additional work is also necessary on the placement of
opinions relative to each other; for example, which opinions are in favor,
against or neutral on a statement, and how are these represented in the
recommendations. This task is extremely complex, even for humans. In the
meantime approximations can be used, for example by considering (spokespersons
of) political parties and their position on the political spectrum. This can
be done through manual annotations, with hardcoded lists of politicians and
their parties, or automatically by for example querying Wikidata for
information on persons identified through Named Entity Recognition. To
calculate the Representation score, we once again use the Kullback-Leibler
Divergence, but this time on the different opinion categories in the
recommendations versus the available pool of data:

$Representation_{(p,q)}=\sum_{o}p(o)log\frac{p(o)}{\tilde{q}(o|u)}$

This calculation is similar to the one in Section 4.1. However, $o$ indicates
the different opinions in the data; $p(o)$ represents the proportion of the
times this opinion was present in the overall pool of data, whereas
$\tilde{q}(o|u)$ represents the proportion of times user $u$ has seen this
opinion in their recommendations. A score of 0 means a perfect match between
the two, which means that the opinions shown in the recommendations are
perfectly representative of those in society. When following the
Participatory’s model _reflective_ point of view we want this value to be as
close to zero as possible, as being representative of society is its main
goal. However, when following one of the other models, we have to make some
alterations on the distributions expressed by $p$. The Critical model’s
_inverse_ point of view aims for the recommendations to diverge as much from
the power relations in society as possible. However, since very small
differences in distributions can result in a very large KL divergence, simply
maximizing the KL divergence is not sufficient. Instead, we inverse the
distribution of opinions present in $p$. Similarly, when choosing the
Deliberative model, we want all opinions in the recommendations to be equally
represented, and therefore we choose $p$ as a uniform distribution of
opinions. This way, for each of the different approaches holds that the closer
the divergence is to zero, the better the recommendations reflect the desired
representation of different opinions. For each of the reflective, inverse and
equal approaches, the aggregated Representation score is obtained by averaging
the Representation score over all recommendations issued to all users.

####  4.4.4. Limitations.

Kullback-Leibler divergence treats each category as being independent, and
does not account for opinions and standpoints that may be more or less similar
to other categories.

###  4.5. Alternative Voices

The _Alternative Voices_ metric measures the relative presence of people from
a minority group. A higher score indicates a proportionally larger presence.

####  4.5.1. Explanation.

Where Representation is largely focused on the explicit content of a
perspective (the _what_), Alternative Voices is more concerned with the person
holding it (the _who_), and specifically whether this person or organisation
is one of a minority or an otherwise marginalised group that is more likely to
be underrepresented in the mainstream media. What exactly entails a minority
is rather vaguely defined. Article 1 from the 1992 United Nations Minorities
Declaration refers to minorities “a non-dominant group of individuals who
share certain national, ethnic, religious or linguistic characteristics which
are different from those of the majority population”, though there is no
internationally agreed-upon definition. In practice, this interpretation is
often extended with gender identity, disability and sexual orientation. A
major challenge of the Alternative Voices metric lies in the actual
identification of a minority voice. Though there are a number of studies that
aim to detect certain characteristics of minorities from textual data, such as
predicting a person’s ethnicity and gender based on their first and last name
(Sood and Laohaprapanon, 2018), there are no approaches that 1) model all
minority characteristics or 2) perform well consistently. This process needs
significant additional and most importantly multidisciplinary research, with a
large focus on ensuring that doing this type of analysis does not lead to
unintended side effects and unwanted consequences. For example, Keyes (2018)
shows that current studies typically treat gender classification as a purely
binary problem, thereby systematically leaving out and wrongly classifying
transgender people. Similarly, Hanna et al. (2020) argues that race and
ethnicity are strongly social constructs that should not be treated as
objective differences between groups. This topic, typically referred to as
(algorithmic) Fairness, is an active research field that aims to counter bias
and discrimination in data-driven computer systems. One thing is for certain:
any recommender system that actively promotes one type of voice over another
should make very explicit on what criteria and following which methods it does
this. Following this both the identification and the way its algorithms use
this information must be fully transparent and auditable. However, for the
remainder of this section we will assume that we do have a proper way of
identifying people from a minority group, either through manual annotation or
automatic extraction.

####  4.5.2. In the context of democratic recommenders.

The Alternative Voices metric is naturally most significant in the Critical
model, which aims to provide a platform to voices that would otherwise go
unheard, and therefore has a large focus on the opinions and perspectives from
minority groups. To a lesser extent, the same holds for the Participatory
model, which aims to foster tolerance and empathy and encourages citizens to
act.

####  4.5.3. Operationalization.

The discussion around Fairness in machine learning systems has lead, among
others, to a number of definitions of the concept. For the operationalization
of Alternative Voices we adapt Equation 10 of Burke et al. (2018) for our
purposes:

$AlternativeVoices=\frac{q^{+}/p^{+}}{q^{-}/p^{-}}$

Here $q^{+}$ denotes the number of mentions of people belonging to a protected
group in the recommendations, whereas $p^{+}$ denotes the number of mentions
of people belonging to a protected group in all the available articles.
$q^{-}$ and $p^{-}$ denote similar mentions, but for people belonging to the
unprotected group. Though the example given in Burke et al. (2018) describes
the equation being used to identify whether loans from protected and
unprotected regions appear equally often, it is also directly applicable to
our notion of Alternative Voices; however, rather than counting regions being
recommended, we count the number of times that people from minority
(protected) versus majority (unprotected) groups are being mentioned in the
news. This function maps to 1 when there is a complete balance between people
from the protected and the unprotected groups; proportionally to the total
number of times they appear in the data set, they appear a similar number of
times in the recommendations. When the value is larger than 1 more people from
unprotected groups appear in the recommendation set, whereas lower than 1
means they appear less.
Again, the aggregate score consists of the average Alternative Voices score
over all recommendations issued to all users.

####  4.5.4. Limitations.

A major caveat of this approach is that it assumes that the mere mentioning of
minority people is enough to serve the goals of the Alternative Voices metric.
This disregards the fact that these people may be mentioned but from another
person’s perspective, or in a negative light. Further research should focus on
not only identifying a person from a minority group, but also whether they are
mentioned as an active or passive agent.

|  | Calibration
---
(topic)
| Calibration
---
(style)
Fragmentation | Affect | Representation | Alternative Voices
Liberal | High | High | High | - | - | -
Participatory | Low | High | Low | Medium | Reflective | Medium
Deliberative | - | - | Low | Low | Equal | -
Critical | - | - | - | High | Inverse | High
Table 1. Overview of the different models and expected value ranges for each
metric. Note that for the metrics reflecting distance of a distribution
(Calibration and Representation), a ”High” target value actually means that
the resulting value should be close to zero.

##  5\. General Limitations

Though all of the metrics described in Section 4 already mention the
limitations of that metric specifically, this section describes a number of
the limitations of this method as a whole.

###  5.1. Ordering

Of the currently specified metrics, only Fragmentation takes the ordering of
the items in the recommendation into account. However, the top result in a
recommendation is of significantly more importance than the result in place
10. In future work, the other metrics should be extended in such a way that
they reflect this.

###  5.2. Engagament and Contestation

One important aspect of the different models that is currently not reflected
in the metrics is the amount of Engagement or more specifically Contestation
they spark in their userbase. Ultimately, the aim of all models is to spark
civic discussions and action, albeit to different ends (e.g. whereas in the
Deliberative model the deliberation itself is a major aim, in the Critical
model engagement and contestation must ultimately result in challenging
established distributions of power and decision routines). This level of
Engagement, which could for example be approached by counting the number of
replies in a discussion thread or the number of shares on social media, can
only be measured after the recommendations have been issued and are often
already a standard part of the online evaluation of the performance of a
recommender system (Beel et al., 2013b).

###  5.3. Formalism Trap

Many of the concepts described here are susceptible to the Formalism Trap
described in (Selbst et al., 2019), which is defined as the _”[f]ailure to
account for the full meaning of social concepts […], which can be procedural,
contextual and contestable, and cannot be resolved through mathematical
formalisms”_. Though our approach aims to model concepts founded in social
science theories, they are merely approximations and to a large extent
simplifications of very complex and nuanced subjects that have been contested
and debated in the social sciences for decades. To claim our approach comes
close to covering these subtleties would be presumptuous - however, we do
believe it is necessary to provide a starting point in the modeling of
concepts that have so far been neglected in the evaluation of news
recommendations. The pitfalls of this trap should be mitigated by always
providing full transparency on how these concepts are implemented, on what
kind of data they are based, and most importantly on how they should (and
should not) be interpreted.

###  5.4. Bias in the dataset

The metrics presented in Section 4 typically rely on measuring a difference
between the set of recommended items and the full set of articles that were
available, the reading history of the user in question or among users. What it
does not do is account for inherent bias in the overall dataset, though the
possibility of exposure diversity depends on the availability of content in
the pool. If the quality and diversity of the pool is low, recommenders have
insufficient options to provide good recommendations. That means exposure
diversity ultimately is dependent on external diversity. Detecting such a bias
in the dataset rather than in the produced recommendations and undertaking
steps to remedy this needs additional work.

###  5.5. Inherent limits to value by design approaches

Finally, it is important to be mindful of another lesson from the general
diversity by design literature, namely that there are also certain limits to
value sensitive design, and for our case: the extent to which diversity as a
normative concept can be operationalized in concrete recommender design. This
can have to do with the sheer difficulty of translating certain aspects of
diversity, but also with the trade-offs between values that optimizing for
exposure diversity can involve. Examples of this are commercial constraints
and the need to optimize for profit rather than for diversity metrics, but
also the limited effectiveness of recommenders in actually steering user
choices. As Friedman et al. (2017) explain: ”a given technology is more
suitable for certain activities and more readily supports certain values while
rendering other activities and values more difficult to realize.”

##  6\. Implementation

We are working on the implementation of the concepts and metrics discussed
here in an open source tool111Link taken out during review. The goal of this
tool is to implement the metrics described in this paper as evaluation metrics
for recommender design, and in doing so enable media companies to evaluate the
performance of their own recommendations against those of several baseline
recommendations.

###  6.1. Approach

By making comparisons between the different recommender approaches, media
companies should be able to draw conclusions about which recommender strategy
fits their mission best. Figure 1 shows an example of what such a comparison
could look like. Here, three different algorithms are compared. Algorithm 1 is
most suitable for recommender systems following the Liberal model, as it is
well-calibrated to the user’s needs. Algorithm 2 is more suited for the
Deliberative model (low Activation and non-diverging Representation) and
algorithm 3 for the Critical model (high Activation and Alternative Voices).
In practice these differences are unlikely to be this distinctive; by also
comparing the performance of these algorithms to very simple recommendation
approaches, such as a random recommender, the media company can also draw
conclusions about where the recommender simply reflects the available data,
and where it significantly influences the type of data that is shown. By
making these visualizations as intuitive as possible, they should facilitate
the discussion between data science teams, editors and upper management around
this topic. To make this approach reusable and broadly applicable, it should
be implemented and tested on both a benchmark set such as (Wu et al., 2020)
and in a real-life setting. We are in contact with multiple media companies,
to inform them about the different models of democracy, facilitate the
discussion around this subject, and stimulate the implementation of our tool.
Simultaneously this topic is continuously being discussed with experts from
many different disciplines, as happened for example during a Dagstuhl
Workshop222link taken out during review.

Figure 1. Example visualization of the proposed metrics within the tool.

###  6.2. Guidelines for adoption

The ultimate goal of this paper is to propose notions that could be
incorporated in recommender system design. In our vision, media companies
could approach this in the following steps:

  1. (1)

Determine which model of democracy to follow
Following the different models described in Section 3, the media company in
question should decide which model of democracy the recommender system should
reflect. This is something that should be decided in active discussion with
the editorial team, and directly in line with the media company’s mission.

  2. (2)

Identify the corresponding metrics
Use Table 1 to determine which metrics are relevant, and what the expected
value range for each metric is. For example, when choosing to follow the
Deliberative model, the recommender system should optimize for a low
Fragmentation, low Activation and equal Representation. Similarly, for the
Critical model, it should optimize for high Activation, inverse Representation
and high Alternative Voices.

  3. (3)

Implement into recommender design
Here it is of key importance to determine the relative importance of each
metric, and how to make a trade-off between recommender accuracy and normative
diversity. For example, Mehrotra et al. (2018) details a number of approaches
to combining Relevance and Fairness in Spotify’s music recommendation
algorithm, and this approach can also be applied in the trade-off between
accuracy and the metrics relevant for the chosen model.

We do not consider these metrics to be the final ”truth” in the identification
of diversity in news recommendations. For example, it is possible and
acceptable that during discussions, a media company decides that they aim to
follow the Deliberative model of democracy. However, rather than focusing on
the equal Representation of opinions in the recommendations, they decide to
focus on providing a good balance of background- and opinion pieces, either
for the sake of practicality or because this is more in line with their
mission. The metrics and their operationalizations should serve as inspiration
and a starting point for discussion, not as restrictions or set requirements
for ”good” recommender design.

##  7\. Discussion

In this paper we have translated normative notions of diversity into five
metrics. Each of the metrics proposed here is relevant in the context of
democratic news recommenders, and combined they form a picture that aims to be
expressive of the nuances in the different models. However there is still a
lot of work to be done, both in terms of technical feasibility and in
undertaking steps to make diversity of central importance for recommender
system development.

At the basis of our work is that we believe diversity is not a single
absolute, but rather an aggregate value with many aspects. In fact, we argue
that what constitutes ’good’ diversity in a recommender system is largely
dependent on its goal, which type of content it aims to promote, and which
model of the normative framework of democracy it aims to follow. As none of
these models is inherently better or worse than the others, we believe that a
media company should take a normative stance and evaluate their recommender
systems accordingly.

Different fields and disciplines may have very different notions of the same
concept, and navigating these differences is a process of constant negotiation
and compromise, but also of expectation management. Abstract concepts such as
diversity may never be fully captured by the cold, hard numbers that
recommender system practitioners are used to. As technological advances take
on an ever more central role in society, the necessity to bridge this gap and
make such concepts more concrete also arises. Social sciences, humanities and
computer science will need to meet in the middle between abstract and
concrete, and work together to create ethical and interpretable technologies.
This work is not a final conclusion on how diversity can be measured in news
recommendations, but rather a first step in forming the bridge between the
normative notion of diversity and its practical implementation.

## References

  * (1)
  * Baker (1998) Edwin C. Baker. 1998\.  _Media Concentration and Democracy: Why Ownership Matters_.  New York: Cambridge University Press.
  * Beel et al. (2013a) Joeran Beel, Marcel Genzmehr, Stefan Langer, Andreas Nürnberger, and Bela Gipp. 2013a.  A Comparative Analysis of Offline and Online Evaluations and Discussion of Research Paper Recommender System Evaluation. In _Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation_ _(RepSys ’13)_. Association for Computing Machinery, New York, NY, USA, 7–14.  https://doi.org/10.1145/2532508.2532511
  * Beel et al. (2013b) Joeran Beel, Marcel Genzmehr, Stefan Langer, Andreas Nürnberger, and Bela Gipp. 2013b.  A Comparative Analysis of Offline and Online Evaluations and Discussion of Research Paper Recommender System Evaluation. In _Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation_ _(RepSys ’13)_. Association for Computing Machinery, New York, NY, USA, 7–14.  https://doi.org/10.1145/2532508.2532511
  * Bodo (2019) Balazs Bodo. 2019\.  Selling News to Audiences – A Qualitative Inquiry into the Emerging Logics of Algorithmic News Personalization in European Quality News Media.  _Digital Journalism_ 0, 0 (2019), 1–22.  https://doi.org/10.1080/21670811.2019.1624185
  * Burke et al. (2018) Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018\.  Balanced neighborhoods for multi-sided fairness in recommendation. In _Conference on Fairness, Accountability and Transparency_. 202–214.
  * Castells et al. (2015) Pablo Castells, Neil J Hurley, and Saul Vargas. 2015\.  Novelty and diversity in recommender systems.  In _Recommender systems handbook_. Springer, 881–918.
  * Chaney et al. (2018) Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018.  How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In _Proceedings of the 12th ACM Conference on Recommender Systems_ _(RecSys ’18)_. Association for Computing Machinery, New York, NY, USA, 224–232.  https://doi.org/10.1145/3240323.3240370
  * Christians (2006) Clifford Christians. 2006\.  The media and moral literacy.  (2006), 62.
  * Christians et al. (2009) Clifford Christians, Theodore L. Glasser, Denis McQuail, Kaarle Nordenstreng, and Robert A. White. 2009.  _Normative theories of the media: Journalism in democratic societies_.  University of Illinois Press.
  * Dahlberg (2011) Lincoln Dahlberg. 2011\.  Re-constructing digital democracy: An outline of four ‘positions’.  _New Media & Society_ 13, 6 (2011), 855–872.  https://doi.org/10.1177/1461444810389569
  * Dillahunt et al. (2015) Tawanna R Dillahunt, Christopher A Brooks, and Samarth Gulati. 2015.  Detecting and visualizing filter bubbles in Google and Bing. In _Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems_. 1851–1856.
  * Dörr (2016) Konstantin Nicholas Dörr. 2016\.  Mapping the field of Algorithmic Journalism.  _Digital Journalism_ 4, 6 (2016), 700–722.  https://doi.org/10.1080/21670811.2015.1096748
  * Eskens et al. (2017) Sarah Eskens, Natali Helberger, and Judith Moeller. 2017\.  Challenged by news personalisation: five perspectives on the right to receive information.  _Journal of Media Law_ 9, 2 (2017), 259–284.  https://doi.org/10.1080/17577632.2017.1387353
  * Feng et al. (2010) Lijun Feng, Martin Jansche, Matt Huenerfauth, and Noémie Elhadad. 2010.  A comparison of features for automatic readability assessment. In _Proceedings of the 23rd international conference on computational linguistics: Posters_. Association for computational linguistics, 276–284.
  * Ferree et al. (2002) Myra Marx Ferree, William A Gamson, Jürgen Gerhards, and Dieter Rucht. 2002.  Four models of the public sphere in modern democracies.  _Theory and society_ 31, 3 (2002), 289–324.
  * Ferrer-Conill and Jr. (2018) Raul Ferrer-Conill and Edson C. Tandoc Jr. 2018.  The Audience-Oriented Editor.  _Digital Journalism_ 6, 4 (2018), 436–453.  https://doi.org/10.1080/21670811.2018.1440972
  * Fredrickson (2013) Barbara L Fredrickson. 2013\.  Positive emotions broaden and build.  In _Advances in experimental social psychology_. Vol. 47. Elsevier, 1–53.
  * Friedman et al. (2017) Batya Friedman, David G. Hendry, and Alan Borning. 2017\.  A Survey of Value Sensitive Design Methods.  _Found. Trends Hum.-Comput. Interact._ 11, 2 (Nov. 2017), 63–125.  https://doi.org/10.1561/1100000015
  * Hanna et al. (2020) Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020\.  Towards a critical race methodology in algorithmic fairness. In _Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency_. 501–512.
  * Helberger (2019) Natali Helberger. 2019\.  On the Democratic Role of News Recommenders.  _Digital Journalism_ 0, 0 (2019), 1–20.  https://doi.org/10.1080/21670811.2019.1623700
  * Held (2006) David Held. 2006\.  _Models of democracy_.  Stanford University Press.
  * Hutto and Gilbert (2014) Clayton J Hutto and Eric Gilbert. 2014.  Vader: A parsimonious rule-based model for sentiment analysis of social media text. In _Eighth international AAAI conference on weblogs and social media_.
  * Jacobs and Wallach (2019) Abigail Z Jacobs and Hanna Wallach. 2019.  Measurement and fairness.  _arXiv preprint arXiv:1912.05511_ (2019).
  * Jannach and Jugovac (2019) Dietmar Jannach and Michael Jugovac. 2019.  Measuring the Business Value of Recommender Systems.  _ACM Trans. Manage. Inf. Syst._ 10, 4, Article 16 (Dec. 2019), 23 pages.  https://doi.org/10.1145/3370082
  * Jr. and Thomas (2015) Edson C. Tandoc Jr. and Ryan J. Thomas. 2015.  The Ethics of Web Analytics.  _Digital Journalism_ 3, 2 (2015), 243–258.  https://doi.org/10.1080/21670811.2014.909122
  * Karppinen ([n.d.]) Kari Karppinen. [n.d.].  Conceptions of democracy in media and communications studies.
  * Karppinen (2013) Kari Karppinen. 2013\.  Uses of Democratic Theory in Media and Communication Studies.  _Observatorio_ 7, 3 (2013), 1–17.
  * Keyes (2018) Os Keyes. 2018.  The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition.  _Proc. ACM Hum.-Comput. Interact._ 2, CSCW, Article 88 (Nov. 2018), 22 pages.  https://doi.org/10.1145/3274357
  * Kim and Oh (2011) Dongwoo Kim and Alice Oh. 2011.  Topic chains for understanding a news corpus. In _International Conference on Intelligent Text Processing and Computational Linguistics_. Springer, 163–176.
  * Kunaver and Pozrl (2017) Matevz Kunaver and Tomaz Pozrl. 2017.  Diversity in recommender systems – A survey.  _Knowledge-Based Systems_ 123 (2017), 154 – 162.  https://doi.org/10.1016/j.knosys.2017.02.009
  * Lewis and Westlund (2015) Seth C. Lewis and Oscar Westlund. 2015.  Big Data and Journalism.  _Digital Journalism_ 3, 3 (2015), 447–466.  https://doi.org/10.1080/21670811.2014.976418
  * Manin (1987) Bernard Manin. 1987\.  On legitimacy and political deliberation.  _Political theory_ 15, 3 (1987), 338–368.
  * McNee et al. (2006) Sean M McNee, John Riedl, and Joseph A Konstan. 2006.  Being accurate is not enough: how accuracy metrics have hurt recommender systems. In _CHI’06 extended abstracts on Human factors in computing systems_. 1097–1101.
  * Mehrotra et al. (2018) Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018\.  Towards a Fair Marketplace: Counterfactual Evaluation of the Trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. In _Proceedings of the 27th ACM International Conference on Information and Knowledge Management_ _(CIKM ’18)_. Association for Computing Machinery, New York, NY, USA, 2243–2251.  https://doi.org/10.1145/3269206.3272027
  * Nguyen et al. (2014) Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014.  Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In _Proceedings of the 23rd International Conference on World Wide Web_. 677–686.  https://doi.org/10.1145/2566486.2568012
  * Nicholls and Bright (2019) Tom Nicholls and Jonathan Bright. 2019.  Understanding News Story Chains using Information Retrieval and Network Clustering Techniques.  _Communication Methods and Measures_ 13, 1 (2019), 43–59.
  * on Artificial Intelligence (2018) High-Level Expert Group on Artificial Intelligence. 2018.  _Draft Ethics guidelines for trustworthy AI_.  Technical Report. European Commission.
  * Papacharissi (2016) Zizi Papacharissi. 2016\.  Affective publics and structures of storytelling: sentiment, events and mediality.  _Information, Communication & Society_ 19, 3 (2016), 307–324.  https://doi.org/10.1080/1369118X.2015.1109697
  * Pareti et al. (2013) Silvia Pareti, Tim O’Keefe, Ioannis Konstas, James R Curran, and Irena Koprinska. 2013\.  Automatically detecting and attributing indirect quotations. In _Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing_. 989–999.
  * Pariser (2011) Eli Pariser. 2011\.  _The filter bubble: How the new personalized web is changing what we read and how we think_.  Penguin.
  * Russell (2003) James A Russell. 2003\.  Core affect and the psychological construction of emotion.  _Psychological review_ 110, 1 (2003), 145.
  * Schuck and Feinholdt (2015) Andreas RT Schuck and Alina Feinholdt. 2015.  News framing effects and emotions.  _Emerging Trends in the Social and Behavioral Sciences: an Interdisciplinary, Searchable, and Linkable Resource_ (2015), 1–15.
  * Selbst et al. (2019) Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019\.  Fairness and Abstraction in Sociotechnical Systems. In _Proceedings of the Conference on Fairness, Accountability, and Transparency_ _(FAT* ’19)_. Association for Computing Machinery, New York, NY, USA, 59–68.  https://doi.org/10.1145/3287560.3287598
  * Sood and Laohaprapanon (2018) Gaurav Sood and Suriyan Laohaprapanon. 2018.  Predicting race and ethnicity from the sequence of characters in a name.  _arXiv preprint arXiv:1805.02109_ (2018).
  * Steck (2018) Harald Steck. 2018\.  Calibrated Recommendations. In _Proceedings of the 12th ACM Conference on Recommender Systems_ _(RecSys ’18)_. Association for Computing Machinery, New York, NY, USA, 154–162.  https://doi.org/10.1145/3240323.3240372
  * Strömbäck (2005) Jesper Strömbäck. 2005\.  In Search of a Standard: four models of democracy and their normative implications for journalism.  _Journalism Studies_ 6, 3 (2005), 331–345.  https://doi.org/10.1080/14616700500131950
  * Thurman et al. (2019) Neil Thurman, Judith Moeller, Natali Helberger, and Damian Trilling. 2019.  My Friends, Editors, Algorithms, and I.  _Digital Journalism_ 7, 4 (2019), 447–469.  https://doi.org/10.1080/21670811.2018.1493936
  * Thurman and Schifferes (2012) N. Thurman and S. Schifferes. 2012.  The Future of Personalisation at News Websites: Lessons from a Longitudinal Study.  _Journalism Studies_ 13, 5-6 (March 2012).  https://doi.org/10.1080/1461670X.2012.664341
  * Van Zomeren et al. (2004) Martijn Van Zomeren, Russell Spears, Agneta H Fischer, and Colin Wayne Leach. 2004.  Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy.  _Journal of personality and social psychology_ 87, 5 (2004), 649.
  * Vargas and Castells (2011) Saúl Vargas and Pablo Castells. 2011.  Rank and relevance in novelty and diversity metrics for recommender systems. In _Proceedings of the fifth ACM conference on Recommender systems_. 109–116.
  * Webber et al. (2010) William Webber, Alistair Moffat, and Justin Zobel. 2010\.  A similarity measure for indefinite rankings.  _ACM Transactions on Information Systems (TOIS)_ 28, 4 (2010), 1–38.
  * Wu et al. (2020) Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020.  MIND: A Large-scale Dataset for News Recommendation.  _ACL_ (2020).
  * Young (1996) Iris Marion Young. 1996\.  Communication and the other: Beyond deliberative democracy.  _Democracy and difference: Contesting the boundaries of the political_ 31 (1996), 120–135.
  * Zaller (2003) John Zaller. 2003\.  A new standard of news quality: Burglar alarms for the monitorial citizen.  _Political Communication_ 20, 2 (2003), 109–130.
  * Ziegler et al. (2005) Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005.  Improving recommendation lists through topic diversification. In _Proceedings of the 14th international conference on World Wide Web_. 22–32.