=Paper=
{{Paper
|id=Vol-2841/PIE+Q_5
|storemode=property
|title=Data Challenges in Disinformation Diffusion Analysis
|pdfUrl=https://ceur-ws.org/Vol-2841/PIE+Q_5.pdf
|volume=Vol-2841
|authors=Paolo Papotti
|dblpUrl=https://dblp.org/rec/conf/edbt/Papotti21
}}
==Data Challenges in Disinformation Diffusion Analysis ==
https://ceur-ws.org/Vol-2841/PIE+Q_5.pdf
Data Challenges in Disinformation Diffusion Analysis
(Abstract)
Paolo Papotti
papotti@eurecom.fr
EURECOM
1 THE NEED FOR BETTER DIFFUSION
NETWORKS
Social media enable fast and widespread dissemination of infor-
mation that can be exploited to effectively spread disinformation
by bad actors [1]. We refer to disinformation as the malicious
and coordinated spread of inaccurate content for manipulation
of narratives1 . It has been showed that social media disinforma-
tion has effectively reached millions of people in state-sponsored
campaigns2 .
Several computational solutions have been proposed for the
identification of coordinated campaign on a single platform [12].
They study how content is disseminated across a network of
inter-connected users. However, two main practical challenges
limit the impact of such approaches.
First, existing approaches focus on a single sources, such as
Twitter or Reddit. Unfortunately, misinformation campaigns span
multiple platforms and there is a recognized need to jointly an- Figure 1: Coordinated campaign for the same content
alyze the diffusion of content across different sources, such as across three social platforms. A node with the same color
social networks, online forums (e.g., Reddit), and traditional news denote the same user, dashed arrows denote manipulated
outlets (e.g., comments in reputable sites). content.
Second, the content diffusion graphs that are currently gener-
ated from social network APIs are limited in quality. For example,
only the information about content re-posting (e.g., re-tweets) of
is just one example of the richer kind of metadata that is needed
a user is directly provided. But information is disseminated also
for better diffusion networks.
by manipulating the original content to add bias, “evidence”, or
In fact, to tackle the first challenge, diffusion networks should
propaganda material. Moreover, fine granularity of the re-posting
be heterogeneous, covering multiple platforms, with the ability to
is not available, with the recognized problem of star-effect for
recognize the same content and the same users across services.
re-tweets that can heavily degrade the quality of the network
In Figure 1, users that refer to the same real world person are
model [13].
annotated with nodes having the same color. Also, to handle the
Consider the example in Figure 1 that shows the coordinated
second challenge, the edges should be typed with fine-grained
sharing of the same initial piece of content (say, a textual news)
metadata that model different actions in the spread of the content.
by three users over different platforms. With the current infras-
We believe that data here plays a role as important as the algo-
tructure and APIs, a journalist or a fact-checker willing to study
rithms used for the analysis and therefore more attention is de-
the diffusion network would look at each network in isolation.
served to the problem of creating such richer diffusion networks.
S/he would be able to follow content across users (nodes) only
Their creation can lead to better identification of coordinated
when they re-post explicitly (full edges across nodes).
efforts [11] and ultimately allow the analysis of disinformation
In this example, the information would not be enough for the
campaigns in terms of actors, space, and time.
early identification of the coordinated campaign started by the
The goal is therefor to develop methods for modeling and
three users. Looking at only one source with limited information
creating the rich diffusion networks from the existing platform
does not enable the analytics, neither in terms of scope nor ev-
APIs. The resulting networks can be exploited to assist users,
idence, that we need to identify and understand how false and
such as fact-checkers and journalists, in
biased content is used in online campaigns [12].
To overcome this limitation, recent approaches explore evi- (1) monitoring sources at scale and recognize misleading in-
dence across users and platforms, such as coordinated link shar- formation (in terms of false or biased textual content) on
ing [7]. While this signal has proven to be useful, we believe this social networks and forum websites;
(2) tracking the spread and diffusion of the content in terms
1 The observations in this work apply also for misinformation, where actors spread of time and actors;
incorrect content unintentionally. (3) generating visualizations that support the fight against
2 E.g., https://transparency.twitter.com/en/reports/information-operations.html
misinformation and related literacy efforts.
© 2021 Copyright for this paper by its author(s). Published in the Workshop Proceed- This network generation is indeed challenging, as the desired
ings of the EDBT/ICDT 2021 Joint Conference (March 23–26, 2021, Nicosia, Cyprus) metadata is not available and hard to profile automatically in an
on CEUR-WS.org. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0)
accurate way. We discuss next two research directions that we
identify as critical to tackle these challenges.
2 RESEARCH DIRECTIONS based on friendship or topic affinity [3], if an node is likely to be
The goal is to develop methods for the automatic modeling of a bot [6], if the content has been manipulated by inserting false
content manipulation and diffusion across time and different claims or bias in the language [5, 10]. In this line of research, it
media sources, such as social networks, forums, and news outlets. seems promising to exploit both linguistic analysis of the text and
Not only we want the diffusion graph for a given content to be external knowledge. The latter could be modelled as reference
across sources and very well described in terms of information, information in relational datasets [10], knowledge-graphs [2],
we also want it (i) to preserve precisely the provenance of the or check corpora [8, 14]. Recent results show that transformer-
data (who created and shared, how and when) and (ii) to be as based language models and query generation techniques can
much as possible automatic in its creation, both to handle the automatically detect text containing false claims3 and therefore
Web scale and to not put additional burden on the users. There provide valuable metadata to enrich the network.
are therefore several challenges that we need to overcome Example 2. Consider again the article example from Example 1.
When it is shared across users, some of them introduce incorrect
• different sources do not contain any readily available in- statistics about its impact (“it works only for young people”), or
formation to connect users/content across networks and facts that are not supported by any source (“it will cost 100$ per
the automatic matching is a difficult task in both cases; dose”). We aim to enrich the network edges by recognizing how
• labeling the content in terms of being false, manipulated, the content goes from its form 𝐴 to a new form 𝐴∗ when it is
and biased requires deep understanding of the language shared by a certain user 𝑢.
and of the reference and background information;
• Web scale implies massive ingestion from heterogeneous We believe that an effective solution to the problem of creat-
data sources, but we would prefer tools that can be used ing diffusion networks for textual content across heterogeneous
by end users on their machines for confidentiality; sources would enable better disinformation campaigns detection.
• support for different languages as we aim at helping users The resulting graph with typed nodes (persons, organizations)
across different countries. and typed relationships (copy or manipulation in terms of con-
tent or form) can be then analyzed with existing methods such
Given the challenges above, a natural first line of work is to as clustering and geometric deep learning [9, 12], or with novel
conduct data integration research to generate a unified represen- methods that take full advantage of the new information and
tation from heterogeneous, non-aligned sources. A second line of better identify emerging coordinated campaigns.
work is to deploy natural language processing (NLP) techniques
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