11institutetext: University of Duisburg-Essen, Duisburg, Germany
11email<EMAIL_ADDRESS>University of Agder,
Kristiansand, Norway 33institutetext: Kristiansand Kommune, Kristiansand,
Norway

# Multi-Platform Framing Analysis: A Case Study of Kristiansand Quran Burning

Anna-Katharina Jung 11 0000-0002-0905-4932 Gautam Kishore Shahi 11
0000-0001-6168-0132 Jennifer Fromm 11 Kari Anne Røysland 22
Kim Henrik Gronert 33

###### Abstract

The framing of events in various media and discourse spaces is crucial in the
era of misinformation and polarization. Many studies, however, are limited to
specific media or networks, disregarding the importance of cross-platform
diffusion. This study overcomes that limitation by conducting a multi-platform
framing analysis on Twitter, YouTube, and traditional media analyzing the 2019
Koran burning in Kristiansand, Norway. It examines media and policy frames and
uncovers network connections through shared URLs. The findings show that
online news emphasizes the incident’s legality, while social media focuses on
its morality, with harsh hate speech prevalent in YouTube comments.
Additionally, YouTube is identified as the most self-contained community,
whereas Twitter is the most open to external inputs.

###### Keywords:

Quran burning, Framing Theory, Frame Analysis, Cross-platform diffusion,
Harmful content

##  1 Introduction

The appearance and rise of social media platforms restructured and diversified
the process of information diffusion. While priorly, the dissemination of
information was limited to traditional media outlets managed by gatekeeping
journalists, nowadays, information can be produced and shared by everyone with
online access [1]. As a result of this development, journalists have not only
lost their major influence on the dissemination of content but also the
sovereignty of interpretation. The classification of content is in the hands
of every single online actor.The process of highlighting certain elements of a
piece of content and promoting a particular understanding and interpretation
is known as the concept of framing [2]. Although the media landscape is
diverse, previous research on framing and information diffusion often focused
only on one specific social media platform or traditional news outlets [3].
With our study, we aim to advance multi-platform framing of societal
incidents. The case of the Quran burning in Kristiansand (Norway) is used as
an example in our study to demonstrate the application and diffusion of media
frames throughout different types of online media platforms. On 16th November
2019, Lars Thorsen - the leader of the Norwegian Anti-Islam group Stop the
Islamification of Norway(SIAN), attempted to burn the Quran on the main square
of Kristiansand. Several persons attacked Lars Thorsen to stop him from
burning the Quran and were arrested by the police. About 300 people witnessed
the incident, and shortly afterwards, videos of the Quran burning and the
attacks circulated on the net. The incident was heavily discussed in both
online news sources and social media. While many members of the Muslim
community described the attackers as defenders of Islam, other actors rather
expressed anti-Islamist views. These different media frames resulted in
tensions within Norwegian society and considerable problems for the Norwegian
government. We aim to answer the following research questions:
RQ1: How was the Quran burning incident in Kristiansand framed on different
social media platforms (Twitter (now X), YouTube) and on online news sites?
RQ2: How is the diffusion between the different social media platforms and
online news sites shaped?
For this multi-platform framing analysis, we analyzed 1,136 tweets, 71 YouTube
videos with 2031 comments, and 109 articles from online news sources. We
distinguish between social media that allows any user to create and share
content and online news sites where editorial teams retain control over the
publication of articles and associated comments [4]. It should be noted that
online news sites differ in the extent to which they follow the ethical code
of practice for the press adopted by the Norwegian Press Association. The
study is organized as follows. First, a short insight into the development and
methodical approaches to the framing theory are presented, followed by a
concise overview of multi-platform framing and cross-platform diffusion.
Afterwards, the methodical approach is presented. Summarizing the case study,
the approach to data collection and cleaning, such as the description of the
coding procedure and code book. This is followed by the presentation of the
results, giving an overview about the distribution of frames on the different
platforms, such as the URL analysis. In the discussion section, connections to
the state of the art presented and prior research are drawn. This discussion
section is followed by a short overview about the limitations of the study and
the ideas for future research. The study is rounded off by the presentation of
the conclusion in which the main findings and the answers to the research
questions are summarized.

##  2 State of the Art

In this section, we describe framing theory, multi-platform framing and Cross
platform information diffusion.

###  2.1 Framing theory

The theory of framing was originally created in the field of sociology in the
1950s and has been refined ever since by various disciplines ranging from
psychology to political and media studies [5, 6, 7]. As well in the more
technical field of information systems, the framing theory has been adapted to
approach the analysis of stakeholder perspectives in a technological context,
or to analyze communication in online environments [8, 3] One of the most
comprehensive definitions of framing was provided by the political scientist
Robert Entman: “Framing essentially involves selection and salience. To frame
is to select some aspects of a perceived reality and make them more salient in
a communication text, in such a way as to promote a particular problem
definition, causal interpretation, moral evaluation and/or treatment
recommendation for the item described” (([2], p. 52). Thus, framing looks not
only at the issues and topics covered by the media but also at the angles
being taken. As diverse as the disciplines that adapted the framing theory are
the methodological approaches that have been developed for its analysis. The
operationalization of frames for empirical studies is complex and challenging,
among other things, due to the huge amount of data that needs to be annotated
(manually) and the influence of personal interpretation of media and its
frames [9]. In prior research, authors divide the existing methodological
approaches to frame analysis in deductive approaches (based on priorly
formulated frames) and inductive approaches (based on frames derived from the
specific data set in focus) [9]. While studies solely based on deductive frame
categories are very systematic and can be easily replicated, they have the
shortcoming that they might ignore important information within the data set
if it does not fit the priorly defined frames [10]. In contrast to that,
inductive approaches use the data and its context as a basis for the creation
of frames and thus are much more sensitive to the peculiarities of specific
research cases [11]. In our study, we applied a mixture of deductive and
inductive approaches to the framing analysis. As a basis, we used the codebook
for analysis of media frames within and across policy issues by [12, 13] and
modified it according to the cultural and thematic context. The codebook will
be explained in more detail in the method section.

###  2.2 Multi-platform framing

While journalists and traditional media houses still have a huge impact on the
shaping of public debates, the influence of online users not bound to news
values and reporting standards has increased with the rise of social networks
and hybrid media systems [14]. This development is described as networked
gatekeeping and networked framing and was defined by [15] as the involvement
of a diversity of online actors from many different backgrounds, including
journalists, activists, non-elite media supporters and regular users, who
gained prominence and attention in their network by effective communicative
and social practices to spread their messages. Networked framing, like
networked gatekeeping, stands for the prominence interpretations received via
crowdsourcing actions [15]. Although [15] value the difference between
gatekeeping and framing in social networks within their own research on the
revolution in Egypt, they fully focus on the discourse on Twitter and thus
miss out on giving an overview of the process on multiple platforms. As well
studies incorporating the idea of networked publics and framing mainly limited
their scope to one platform, as for example [16] who mapped the discussion on
the Iranian presidential elections 2017 on Twitter [16], depicting the
different groups of online elites discussing the subject or previous research
[17] which analyzed the discourse on migration during the Canadian elections
2019. Another recent example is the analysis of [18] who analyzed the
hijacking and reframing of the MeToo hashtag by right-wing actors in a multi-
national Twitter analysis. While [18] do involve different languages and thus
different national communities on Twitter, they did disregard other media
outlets than Twitter. One of the very few studies which pays attention to the
research gap of multi-platform framing is [19], which analyzed the discourse
about the refugee crisis in the Finish online news media and social media
[19]. In contrast to the present study, a computational topic and framing
detection were applied, and a latent Dirichlet allocation algorithm was used.
While the scope of our study is similar, the methodical approaches differ from
each other.

###  2.3 Cross platform information diffusion

The sharing and presentation of frames within one network and across platform
boarders can be as well described as a form of information diffusion.
Information diffusion can be defined “as the process by which a piece of
information (knowledge) is spread and reaches individuals through
interactions” [20]. On social media, this process depends on individuals who
spread information through retweets, shares and likes. The information created
by verified accounts spreads faster than non-verified sources [21, 22]. One
stream of information diffusion research takes a micro perspective and aims to
understand why individual users distribute information [23, 24]. For example,
authors in prior research [24] found that learning and social engagement are
the most important motivations for content sharing [24]. Another stream of
research examines the phenomenon from a macro perspective, focusing on
predicting how information spreads through a social network. These studies
often aim to assess and improve information diffusion models such as
information cascade or threshold models [25, 26, 27, 28]. To understand
information diffusion, scholars highlighted the importance of analyzing the
interplay of different actor and content characteristics [29]. Actor-centered
studies demonstrated the power of opinion leaders in the information diffusion
process [30, 31] and distinguish between different roles such as information
starters, information amplifiers, and information transmitters [32]. Other
studies rather highlighted the impact of content characteristics such as
emotionality [33] or the attachment of images and videos [3]. Notably, most
previous studies focused on a single platform such as Twitter, thereby
neglecting the reality of information diffusion, as is also the case for
networked framing. In this regard, [34] already argued that social media users
can share content from Facebook to Twitter and vice versa, pointing toward the
vanishing boundaries between different social media platforms. A study
proposed that sharing viral videos on alternative platforms might affect their
popularity on the original platform [35]. Furthermore, scholars found a
significant influence of mass media and external websites on information
diffusion within a social media platform [36]. This phenomenon is also known
as the spill-over effect among communication scientists [37]. The detection of
spill-over effects represents a methodological challenge as it is difficult to
find the origin of information in the online media sphere [38]. The authors
suggested using a crawler to identify URLs linking to other online information
sources. Jung et al. [3] built upon this methodological approach and
demonstrated in a case study that online news sites referenced more frequently
to information from Twitter than vice versa. With our study, we aim to extend
the scarce research body on multi-platform networked framing and cross-
platform diffusion by examining the occurrence and diffusion of frames within
and across social media and online media.

##  3 Research Design

In this section, we explain the steps involved in performing the study. They
are discussed below.

###  3.1 Data Collection

In this section, we explain the data collection from different platforms. For
a holistic picture of the online discourse, Twitter and YouTube, as well as
Norwegian newspapers, were chosen. While Twitter is known for (textual)
breaking news content, YouTube is the most prominent platform for video
content. Therefore, these two social networks were considered especially
useful for this study. The time frame for the data collection was 12th
November 2019 until 30th November 2019. A detailed explanation of each
platform is given below. For all platforms, we decided that the keyword SIAN,
as the organizer of the event, such as the keywords Arne Tumyr and Lars
Thorsen, as the involved SIAN leaders, were relevant. Furthermore, the keyword
koranbrenning was chosen as it was the most prevalent term to describe the
event in the Norwegian news. Finally Kristiansand was chosen as a keyword, as
the incident’s location. The results of the data collection confirmed that the
keywords delivered relevant results, wherefore the keyword selection was not
further adjusted.

####  3.1.1 Twitter Data

The Twitter data was gathered with a self-developed Python crawler, which
connects to the Twitter Search Application Programming Interface (API) before
the commercialization of Twitter data) and collects tweets using keywords
SIAN, Arne Tumyr, Lars Thorsen, koranbrenning. The tool collected all
Norwegian tweets, retweets, and replies that contained at least one of these
keywords and were published from 12th November 2019 until 30th November 2019.
We manually checked the search results for relevance and excluded 57 tweets
that were not related to the Quran burning in Kristiansand, excluding tweets
about another Quran burning that happened in Sweden or tweets about other
activities of SIAN, Arne Tumyr, and Lars Thorsen. The final dataset included
2,267 tweets consisting of 865 original tweets, 1,131 retweets, 224 replies,
and 47 commented retweets. The 1,131 retweets were excluded from analysis and
treated as duplicates as they did not add new frames to the Twitter discourse.
The final Twitter data set contained 1,136 tweets.

####  3.1.2 News Paper Articles

For tracking the news media, we used commercial software by M-brain (now
Valona111https://valonaintelligence.com/) to monitor media, ensuring
comprehensive news coverage. It provided us with openly accessible content and
those behind online paywalls. We used the search terms "koranbrenning" and
"Kristiansand" to browse the content from the news media. M-brain searched all
Norwegian media outlets and returned the articles which match the search
terms. From the obtained results, we further filtered the news articles which
contained any of the search terms "Lars Thorsen", "Arne Tumyr", or "SIAN". The
final news media data set included 115 news articles that had been published
between 12th November 2019 and 30th November 2019. After duplicates were
deleted, 109 articles remained for the analysis. The data set included the
article heading, URL to the news article, and date of publication, and we
crawled the content of the news article. The media outlets can be categorized
into three groups: State-owned and mainstream media, Online and Independent
Media and Special Interest and Non-News Platforms. In addition, we also added
to this classification who owned the media outlet and their affiliation to the
Norwegian Press Organization, such additional notes if applicable. For a
detailed overview please refer to table 2 in the Appendix.

####  3.1.3 YouTube Videos and Comments

For the YouTube analysis, we collected videos and comments related to the
Quran Burning incident. To identify the relevant videos, we used the YouTube
keyword search and conducted four different manual searches of the keywords
“SIAN Norway,” “Arne Tumyr,” “Lars Thorsen,” and “Koranbrenning.” The
relevancy was assessed by reading the title and description and watching the
video. Some videos were excluded, for example, when the videos were related to
a different Quran burning incident. Videos that occurred in multiple searches
because they included several keywords were included only once. Overall, we
excluded five duplicate videos. We identified 112 relevant videos in total.
Out of them, 71 videos had comments. We crawled all 8,917 comments related to
those videos. As the total number of comments was too extensive for a manual
assessment, we applied a sampling approach to all videos with more than 100
comments. Ten videos had more than 100 comments, and 62 videos had less than
100 comments. For the sampling, we took all comments from the videos with less
than 100 comments, and for videos that had more than 100 comments, we sampled
100 comments from each video. Overall, we got 2,041 comments from 71 videos in
total. This approach provided us with a diverse data set of all comments from
all videos. In contrast to the newspaper articles and tweets regarding the
Collection of YouTube videos and comments, there was no language restriction
to Norwegian and English, but all videos and comments have been analysed with
help of the subtitles provided by YouTube and translation software whenever
possible.

###  3.2 Data Analysis and Preprocessing

In this section, we describe the steps involved in the data preprocessing and
analysis. We have applied a series of steps to clean our data set. We filtered
the URLs from the text of tweets, YouTube comments, and news articles for data
cleaning following the approach mentioned in [39]. After that, we manually
identified the domain of the URLs to identify the link target platforms (i.e.,
Twitter, YouTube, online news sources, and others). The category others
included links leading to other social media and online news sources that we
have not included in our sample (e.g., Facebook, Instagram, religious
websites).

###  3.3 Frame Analysis

As the Quran Burning incident in Kristiansand can be classified as a
politically motivated event, we decided to use an existing codebook for the
analysis of policy frames by [12, 13]. The codebook has been developed and
validated with a pilot study covering three major events in the US. We used
the most recent version of the codebook, which was updated in 2016. The
codebook was developed for the analysis of policy debates in the United States
and consists of 15 frame dimensions. The 15 frame dimensions have been adapted
according to the Norwegian context and the context of the Quran Burning
incident. Each dimension was equipped with a short paragraph about the
relevance of the Quran Burning case, possible keywords, and examples, which
can be found in Table 1 in Appendix. As the data from Twitter, YouTube and
newspaper articles confronted us with different conditions the codebook was
slightly adapted for each medium, which will be explained along the
description of the coding process. As a first step of the framing analysis,
the coders intensively studied the codebook and then added relevant
paragraphs, keywords, and examples. In the next step the coders have been
provided with the data sets and their English translations. After reading the
text, the coders needed to decide if the translation was understandable or if
there was further clarification needed. In case there was a better translation
of Norwegian data required, the Norwegian team members were involved. If the
translation was understandable, coders needed to decide if the information was
relevant for the respective case study. To understand the YouTube videos, the
subtitle function was used if available, and the title and description were
translated with the help of Google Translate. Afterwards, the relevance of the
data was evaluated. The first relevant criterion was whether the tweet, video
or comment was about or in connection with the Quran burning in Kristiansand.
If this was not the case, it was not deemed to be relevant. Exceptions were
made if the covered international incident was a reaction to the Quran Burning
in Kristiansand. Messages which only contained an emoticon or random strings
of characters were marked as irrelevant. The tweets of the Twitter data set
have been sorted according to the respective tweet ID in order to identify the
conversational threads to which they belonged. Each tweet, YouTube video,
YouTube comment, or newspaper article received one primary frame. The frame
could vary from the original tweet in case of commented retweets and replies.
In case of more complex messages, especially regarding newspaper articles a
secondary frame was chosen if it was not possible to reduce the main messages
of the text to one primary frame. Each data set was coded by two coders. First
of all, 20% of each data set was coded independently to check the intercoder
reliability of each coding. The intercoder reliability was calculated in the
form of Cohen’s Kappa coefficient which can be found in Table 4 of Appendix.
If the result was satisfying, the coding process was continued. In case of
deviations in the first coding round, a third coder was involved, and the
majority rule has been applied. The rest of the data sets were divided between
the first two coders.

##  4 Result

In this section, result obtained from different analysis is provided.

###  4.1 Framing Analysis

With help of the code book we identified the different frames, which have been
used in the discourse and media coverage about the Quran burning incident in
Kristiansand. We have seen a distinct usage of frames in news articles, tweets
and YouTube videos and comments. A detailed description of annotated frames is
given in Table 3 in the Appendix. For the news articles, primary frames and
secondary frames have been applied regularly, as the longer texts often did
not allow being reduced to one single frame. For the coding of the YouTube
comments the frame dimension "None" was applied more frequently than in
comparison to the other media types. By having a closer look at this frame we
realized that this was the case because many of the comments could not be
translated for analysis and the automated translation had reached its limits.
In the 109 coded news articles, the frame dimension Legality,
Constitutionality & Jurisdiction was most frequently used, with 50 (45.9 %)
references. That means that most of the news articles had legal issues as
their main subject in the form of references to freedom of speech or
constitutional issues. Very prevalent was the discussion if it was legal,
according to the Norwegian constitution, to burn the Quran. One example of the
use of the legality frame dimension is an article by the online news outlet
document.no, with the title “It should not be allowed to burn holy books”. The
article discussed if the decision by the Police Directorate to stop SIAN from
burning a copy of the Quran, as a violation of section 185 of the Penal Code
on the prohibition of hate speech, was well grounded. The second most applied
frame dimension in the news article data set was the External Regulation and
Reputation frame with 12%. The incident in Kristiansand led to direct
reactions in the Muslim world, especially Pakistan. The articles in which this
frame was used often discussed the implications of the Quran burning for the
Norwegian telephone provider Telenor, which owns more than a quarter of the
Pakistani cellular market [38]. The public service broadcaster NRK published,
for example, an article with the title “Call for boycott of Norwegian
companies after Quran burning”. The article described the reactions in the
Pakistani community and media landscape to boycott Norwegian products and
especially the Pakistani branch of the Norwegian telephone provider Telenor.
In addition to that, articles with the External Regulation and Reputation
frame described the political reactions to the incident in the form of an
appointment of the Norwegian ambassador in Pakistan for a statement on the
case by Prime Minister Imran Khan and the Ministry of Foreign Affairs. An
example for that is the article “Norway is called on the carpet” (the
Norwegian proverb to call someone to the carpet, can be translated with “to
scold someone”) by the Norwegian daily newspaper Dagbladet. It discussed that
the Pakistani Ministry of Foreign Affairs scolded Norway’s ambassador and
expressed concern that a Quran was set on fire in Kristiansand. The frame
dimension Security and Defense was with 10.1% also applied regularly in the
news article data set. The articles, which contained this primary frame,
discussed extensively that the Quran burning incident resulted in a concrete
security threat for the Norwegian state. One outlet which used this frame is
e.. the news site resett.no, which is a controversial online news site in
Norway, known for its skepticism regarding immigration and Islam. Resett.no is
not part of the Norwegian Press Organization due to the fact that the
organization has deemed that they do not adhere to the Norwegian Press Codex.
The article of resett.no using the Security and Defense frame has the title:
“Threatened to kill after Quran burning: Kill him please!” It discussed that
the burning of the Quran has created violent reactions both on social media,
but also from Muslim communities in Norway. The article stated that several
Muslim leaders in Agder warned that they will now report SIAN to the police.
Besides that, the Security and Defense frame was applied in articles, which
reported about protest actions, which were triggered by the incident. One
example for that is the article “The Norwegian flag is burned in protest
against the burning of the Quran ” by the local newspaper Fedrelandsvennen,
which reported that the Norwegian flag was burned during demonstrations in
different Muslim countries among others in the state of Karachi in Pakistan.
The authors interpreted these incidents as a sign of a concrete threat against
the Norwegian state to suffer an Islamist-motivated attack. Due to the
complexity of the newspaper articles in several cases a secondary frame was
chosen. The most common secondary frame was again Legality, Constitutionality
& Jurisdiction with (32%), which underlines the importance of the legality
frame in the news article data set. Besides that as well the Morality and
Ethics (18%), Political Factors and Implications (16%) and Security and
Defense (14%) frames played an important role as secondary frames. The article
“Koran burning, blasphemy, freedom of speech and hate speech” is an example of
an article with a primary and a secondary frame. The primary frame is
Legality, Constitutionality & Jurisdiction and the secondary frame is Morality
and Ethics. The article discusses both the legal interpretation of the
incident and its morality, following the stance that not everything which is
legal is as well morally defensible.

###  4.2 Tweets

Of the 1,136 analyzed tweets, 47 tweets were coded as irrelevant, which are 4%
of the data set. Among the relevant tweets, the Morality and Ethics frame was
with 28.1% the top frame. An anchor example for this category is: “I am not a
true follower, but what if the Bible was burned? Would antifa violence
responders react? I doubt.”. In addition to the Morality and Ethics frame the
Political Factors and Implications frame dimension was used in 19.2% of the
sample. There have been a lot of comments after the event which underlined a
political motivation. Both lay people and politicians used the burning of the
Quran to voice political views, coming from different political camps. Several
accounts pointed out that they have the impression that SIAN received more
attention, that it deserved according to their overall importance for the
Norwegian political landscape. One anchor example for that is: “Given that the
only Norwegian party of significance that is close to SIAN has just made a
historically poor municipal election, SIAN should not imagine that they have
any significant support.” The accounts, who advocated for SIAN often pointed
out that SIAN is a relevant political group. In the following example SIAN is
presented as a moderate, non violent group in comparison to political groups
of the left political spectrum: “Then you should stop using the word extremist
about SIAN. They have never resorted to violence, which your dear Communists
in Red and Antifa constantly do”. Besides that, only the Legality frame
reached a relevant number of tweets with 13.6%. Two examples for the usage of
the Legality, Constitutionality & Jurisdiction frame are:
“Penal Code §185 used against burning of the Quran? Seems like the law
stretches quite a bit.” and “Norwegian scandal: Freedom of speech is something
we play. The police had received a secret illegal order to stop "violation of
the Qur’an" when the SIAN (Stand Islamization of Norway) demonstration in
Kristiansand was brutally interrupted by local police.” For annoated tweets
without none frame, around 16% (191 tweets) of all tweets referred to external
media sources. 55 of those tweets have been published by media organizations
themselves, the others were shared by mainly individual accounts and some
organizations. As these tweets neutrally shared media links, with short
article snippets instead the content of the shared media content was coded for
the analysis. All other frames covered only in between 0.2% and 7.3% of the
data set and are thus not described in more detail. A second frame added to
less than 5% of the tweets and can thus be ignored.

###  4.3 YouTube videos

All YouTube videos that only showed the incident as a whole without commenting
on it were coded as None if there was no frame included either in the title or
description of the video. The Morality and Ethics frame was the most prevalent
frame used in the YouTube videos, with 36.6%. However, a minor number of
videos applied to the External Regulations & Reputation frame, which together
account for 9.9% of the data set.

Figure 1: Annotated YouTube videos(From left to right video a,b,c)

The video in Figure 1 (a) was uploaded to YouTube by channel JHUNJHUNU PRIME
TIME, joined YouTube on the 7th of November 2019, only a few days prior to the
incident. Even if the account is supposed to give the impression of an
official station due to the name and the chosen avatar, it can be assumed that
it is not an official source but a channel run by a private person. Another
indicator for that is that the channel description is incomplete. Another
example for the use of the morality frame is (b) in Figure 1 a video of the
leader of the Norwegian PEGIDA (Patriotic Europeans against the Islamization
of the Occident). In his video with the title “SIAN’s Koran burning in
Kristiansand; what does the quran say?“, which he prepared in reaction to the
SIAN burning of the Quran in Kristiansand, he presents his interpretation of
the five pillars of the Quran. This video is a good anchor example for videos
which have been created by sympathizers of SIAN. The videos, which belonged to
the External Regulation and Reputation frame, often showed and described
protests, which came up in Muslim countries, especially Pakistan, after the
incident. An anchor example can be the following video. This video (c) in
Figure 1 was posted by the account Pinpoint Pakistan, which has a relatively
professional appearance, such as JHUNJHUNU PRIME TIME. The channel contains
more information about its scope and gives a contact email for questions.
However, there is only a YouTube and Facebook Page with the name of this
organization and no official media house under this name, which as well gives
the impression that it is a media channel run by private persons. It needs to
be doubted that the video thus has been produced by the channel owners
themselves.

###  4.4 YouTube comments

Coding the YouTube comments, we used fewer frames than the other channels,
pointing as well at the limitations of using a predefined codebook. The frames
that were most used were Morality and Ethics, None and Other. The reason why
Morality and Ethics was the most frequently used, was the reference to
religion, e.g.: “Long live Allah.” Often the morality frame was also used to
express beliefs in a hateful manner: “I pissed in koran and ur momz!”. In the
frame Other we have put comments that did not fit in any other category, but
were still relevant. Often there was shortly formulated support or dislike
formulated in a very short manner like “Good” or “Nice” or “lionhearted” or
from the SIAN supporters: “Thanks Lars”. Some have also been hate speech like:
“Look at the red pig he looks like”. The reason why so many comments were
coded as none, was because the content either did not make sense, or we did
not understand the meaning of it. While some meaning might have been lost in
translation, there have been comments including random combinations of
letters. We also saw 5.2% of comments about Norway’s reputation, so we coded
these External Regulation and Reputation. Examples are “Fuck Norway, boycott
Norway”, “Norwegians are bastards” and “Shame on Norway”. This could have
harmed Norway’s reputation and were often as well in a hate speech manner.

###  4.5 URL Analysis

We coded 888 tweets, 27 YouTube comments, and 68 news articles during the
manual framing analysis, including reference hyperlinks from one of the other
platforms. However, some tweets, comments, and articles included multiple
URLs. Summing up, those that included more than one URL led to the total
number of 988 URLs from tweets, 27 from YouTube comments, and 456 URLs from
news articles from the above spillover links. If there were multiple URLs from
one tweet, YouTube comment, or newspaper article to another platform, we split
it into different URLs, so only a link is associated with each source and the
target node. We further analyzed the domain of the URLs to get information
about the spillover of hyperlinks. To identify cross-platform diffusion and
spillover effects, we categorize the URLs into four categories based on
domains: Twitter, YouTube, News Media, and others (which include other domains
apart from the above three, like social media platforms Facebook and
Instagram.). Figure 2 describes the number of different URLs shared from one
platform to another. The figure shows the diffusion of URLs from each platform
to four different categories: Twitter to news media, YouTube, Twitter and
others. Overall, news articles contain multiple URLS within the articles,
mainly referring to other news articles. On Twitter the tweet authors as well
use references within the own newtork boundaries by referencing tweets, but as
well share URLS of news media and YouTube. In contrast YouTube contains
limited URLS, mainly staying within own network boundaries.

Within our collected datasets, we analysed the spillover of URLs among news
articles, Twitter and YouTube. We matched URLs of news articles to Twitter and
YouTube. A spillover of URLs from news article to another is observed mainly
on Twitter; around 26% unique news articles are posted 171 times on Twitter
from domains such as utrop.no, resett.no, gjenstridig.no, nrk.no, dagladet.no,
document.no, afternposten,vg.no, rights.no, nrk.no. At the same time, only two
news articles are shared on YouTube comments, which are not explicitly from
our datasets. For cross-platform, 17 YouTube videos are shared on Twitter. In
contrast, among YouTube comments, only a few URLs are mentioned in comments,
mainly on other YouTube videos and some other social media platforms such as
Facebook and VK.

Figure 2: Distribution of URLs and their source on three different platforms.

##  5 Discussion

This study offers insights about two main topics: differences of multi
platform framing of a social incident on social media (Twitter and YouTube)
and online news sites (RQ1) and cross-platform diffusion of topics and frames
(RQ2). The content analysis of the online news site articles, tweets, and
YouTube videos and comments resulted in a distinctive application of frame
dimensions on the different platforms. Of the 14 main frame dimensions adapted
from Boydstun et al. [12], only four frame dimensions reached a threshold of
at least 10% in one of the data sets, namely Legality, Morality, External
relations, Political factors and the two dimensions None and Other. While the
legality frame dominated the discourse on online news sites, the morality
frame dominated the two social media data sets. We argue that the high
dominance of the morality frame in the social media data sets is related to
the higher level of personal communication on social media platforms, which
allows each individual and organization to contribute to the discussion [1].
According to [40] moral intuition forms the very basis of any normative
evaluation [40]. It is an unfiltered and non-reflected reaction if an incident
should be classified as right or wrong [41]. We argue that the use of the
morality and ethics frame might be more prevalent in social media as
individual users are more likely to express their moral intuition than
(professional) journalists. Further, we considered international sources in
the YouTube data set too, wherefore here we also see reactions from people
living in Muslim countries like Pakistan, feeling morally offended. The
harshest formulation of personal opinions was present in the analyzed YouTube
comments, which involved only the frame dimensions of morality and other. The
other category was used particularly frequently for YouTube comments,
including hate speech which could not be assigned to any of the existing frame
dimensions. Furthermore, YouTube comments showed the highest level of
polarization, particularly within the morality frame. This polarization is
reflected by potential echo chambers of the Muslim supporters and right-wing
supporters, predominantly encountering viewpoints that reinforce their
existing beliefs, which might intensify polarization. The dominance of the
morality frame, which provokes strong emotional responses, contributes to this
phenomenon. Polarization can have broader societal impacts, including
increased social tensions and greater distance between different groups or
countries. The problem of misinformation, which is linked to highly polarized
discourse environments, extends beyond false information to the intensive
diffusion of specific frames and narratives that shape public beliefs and
perceptions [42]. According to Starbird, these frames influence the evidence
used in sensemaking processes and guide interpretations and the formation of
public opinion. Our results suggest that the frames and narratives surrounding
the Quran burning event may overshadow existing evidence, exacerbating
societal and political polarization. However, the analysis of the Twitter
comments revealed as well the limitations regarding the application of
Boydstun’s codebook on YouTube comments. In further research, a more specific
codebook should be developed for this medium and discourse space. The fact
that that polarization was less prevalent on Twitter could potentially be
explained by the different user groups of the platforms, or differently strict
or successful countermeasures of the platforms regarding hate speech [43]. The
deletion of hate speech should be practiced more strongly by YouTube. Not only
at the video level but, above all, in the comments, where AI-supported
approaches may be used. The perspective in the YouTube comments was very
unified and contained mainly narratives like the attackers of SIAN are heroes,
Islam is a religion that should be praised, and Norway should be reproached
for not respecting and protecting Islam. We argue that due to the language
barriers, as most of these comments have been originally published in Urdu or
Arabic, there was little variation and counterspeech of different opinion
camps. Research has shown that users do interact barely with textual posts in
foreign languages [44]. While passive interactions in the form of likes
happen, commenting takes place very rarely [43]. Therefore, we assume that
Norwegian or English-speaking YouTube users have not engaged and reacted with
the comments in Arabic or Urdu, which explains the non-existent counter
speech. It also explains a different application and diffusion of frames
within different user/language groups and shows that the idea of networked
publics and the networked framing has certain boundaries [15]. However, the
traditional media act as a bridge actor here, presenting the incidents of
hatred from social media in other countries such as official reactions from
countries such as Pakistan (External relations frame), which underlines the
role of traditional news media in cross platform frame diffusion. In contrast
to YouTube and the online news sources the discourse on Twitter was the most
diverse according to the variety of applied frames, although the threshold of
10% was not exceeded for all frame dimensions. It indicates that Twitter might
represent a more diverse discourse space compared to the other platforms.
However, this should always be considered with the caveat that the topic
itself may also have influenced how it was discussed on which platform. For
further generalizations future studies are indispensable. Based on the
Kristiansand incident, we perceived it as unexpected that meta-discourses
under the frames of Fairness and Equality or Cultural Identity did not play a
serious role in the data set. There was also no difference in the online news
sources, which offer more space for societal discourses. Instead of an overall
societal discussion about the role of Islam and Muslim groups in Norwegian
society, the discussion was more focused on the personal moral classification
of the conflict, or in online news sources on the legality of the committed
act. This points to a potential question for future research to which extent
social networks allow and foster meta-discourses or whether these are lost in
the mass of personal statements. After analyzing the distribution of the
different frames among the three analyzed platforms (Twitter, YouTube and
online news sites), the analysis of URLs used in the data set was implemented
to answer RQ2. First, we categorized the links originating from a platform
according to their platform type: Twitter, YouTube, news articles, and others.
The category others included all links that did not fit the three main
categories and included i.e. references to Instagram, Facebook, and religious
websites (e.g. islam.net). We observed that a significant amount of URLs
diffusion is happening within the same platform for news articles and YouTube
comments. While around 67% of all URLs in YouTube comments point to other
YouTube content, 72% of all online news sources point to other online news
sources while only 39% of URLs mentioned in tweets refer to other tweets.
Around 47% of URLs mentioned in collected news articles refer to the same
media house, which indicates that online news sources are more likely to share
sources from their own company to strengthen their own economic goals of
longer dwell time and clicks. Solely on Twitter, 52% URLs mentioned in tweets
referes to news media, 3% to YouTube, 39% to other tweets and 8% to other
platforms . This strengthens the ideas of [36, 31], who identified news media
as influential drivers of diffusion in social networks. In contrast, the URLs
of news sites in the Quran Burning case study only included roughly 8% of the
URLs pointing to different social media sites, including Twitter and Facebook.
This contradicts the findings of Jung (2018) [3], who found out that news
media were referencing Twitter more often than Twitter posts referred to news
media. However, Jung (2018) [3] did not use a data set looking at one specific
case study but a broader variation of topics. Although the diffusion and
referencing within the respective platform boundaries are high, cross-platform
diffusion makes up 25-60% of the sub datasets of this case study,
strengthening the results of [34] and [35], who both found an interdependence
between the degree of diffusion and cross-platform spill-overs. While for the
spill over of news articles to Twitter it could be identified by our coding,
that in many cases shared news articles also led to a share of frames, as
snippets of the articles formed as well part of the tweet, it remains unclear
is if the other reference spill-overs which have been identified in the data
sets do also directly lead to a frame spill-over, which could give insights
into the importance of the different platforms and actors involved in the
frame-setting and agenda-building process [38].

##  6 Limitations and Future Research

One major limitation of our keyword-based data collection approach is that
some relevant data about the Quran burning incident in Kristiansand might have
been missed, compromising completeness. Furthermore, the fact that an existing
codebook was adapted, may be another methodical limitation. A codebook derived
from the data set always has the strength of being more case-related and
specific. While the Twitter data and YouTube videos could be easily coded with
the media corpus codebook, We see the limitations of the codebook for YouTube
comments and understand an adoption of it for YouTube comments as a potential
future research endeavor. We admit that there is a certain language bias in
relation to the collection of YouTube videos and comments in our analysis.
However, we argue that videos are more accessible across languages compared to
text alone, and AI-generated subtitles on YouTube were sufficiently accurate
for our analysis. In addition to the limitations the study also stimulates
many new ideas for further research. First, in a follow-up study, it would
make sense to code not only the dataset collected in the first step, but also
all the third-party content identified by the URL analysis on the three
platforms. Through this snowball system, it would be possible to make clearer
statements about frame spill-overs in the future. This further analysis could
for example be supported by a network analysis to visualize the spill-overs in
the different directions and could help to identify the main frame-setters.
Furthermore, it would be interesting to investigate whether sockpuppeting or
astroturfing took place to push certain frames (e.g. by following the approach
presented in [44]. Furthermore, it would also be appropriate to focus on the
questions raised in the discussion of this article on the topic of discourse
diversity on different platforms and the possibility of meta-discourses in
social networks.

##  7 Conclusion

This study contributes to closing the research gap of networked framing on
multiple platforms by analyzing how the socially and politically relevant
incident of the Quran burning by the Anti-Islamic group SIAN in Kristiansand
was framed on Twitter, YouTube and in online news sources. The analysis
revealed that on social media the frame dimension of morality and ethics was
highly dominant, while on online news sites the legality frame played the most
relevant role. The higher use of the morality frame on social networks can be
related to two main reasons: the higher number of personal statements in
social networks and the involvement of users from Islamic states like Pakistan
who were morally concerned by the incident. Another finding was the high
appearance of hate speech in the form of the other frame dimension in the
YouTube comments, which was not similarly present on other platforms. As most
hate speech was published in Urdu and Arabic there was no counter-speech in
English or Norwegian, underlining a lack of interaction between different
language groups and ergo a limited diffusion of frames between different
language groups. Which underlines the limits of the proposed concept of
networked framing and gatekeeping by [15]. Although the internationality of
the social media users was higher on YouTube, the frame dimension of External
regulations and & Reputation was more relevant in online news sources.
However, online news sources were highly dominated by the legality frame,
discussing the legal classification of the incident in accordance with the
Norwegian law, which did only play a minor role on social networking sites.
The highest diversity of frame dimensions was applied by the Twitter community
pointing at a more manifold discussion of the topic on this platform. However,
meta-discourses on the fairness of the treatment of minority groups or a
discussion on the cultural identity of Norwegian society in relation to the
incident have been absent. Concerning the diffusion of content regarding the
Quran Burning incident between different social media platforms and online
news sites, it could be revealed that most diffusion takes place within the
specific network boundaries. On Twitter, the highest amount of content
generated outside the platform boundaries was shared. The most self-contained
platform in this study was YouTube, which leads to the assumption that, at
least for this case study, YouTube has a limited networked public solely
looking at the content created by its own community. Cross-platform diffusion
forms between a quarter to two thirds of the disseminated content, which
underlines the interconnectedness and influence between the different
discourse spaces and platforms. The study underscores the discursive power of
social network users, who do not solely copy the frames of traditional media
one-to-one but apply different and more multifaceted frames in their
reflections. The greatest linkage was found between Twitter and online news
sources for which we could identify not only a reference- but as well frame
spill-over, as to a great extent frames of the news sources were spilled in
form of text snippets and uncommented URLS into the Twitter community.

## Acknowledgements

The author(s) disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: This project has
received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie grant agreement No
823866.

##  8 Appendix

See pages - of AppendixMisdoomRevision.pdf

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