=Paper=
{{Paper
|id=Vol-2482/paper35
|storemode=property
|title=2nd International Workshop on Rumours and Deception
in Social Media: Preface
|pdfUrl=https://ceur-ws.org/Vol-2482/paper35.pdf
|volume=Vol-2482
|authors=Ahmet Aker,Arkaitz Zubiaga,Kalina Bontcheva,Maria Liakata,Rob Procter
|dblpUrl=https://dblp.org/rec/conf/cikm/AkerZBLP18
}}
==2nd International Workshop on Rumours and Deception
in Social Media: Preface==
https://ceur-ws.org/Vol-2482/paper35.pdf
2nd International Workshop on Rumours and Deception
in Social Media: Preface
Ahmet Aker Arkaitz Zubiaga
University of Duisburg-Essen, Germany Queen Mary University of London, UK
University of Sheffield, UK a.zubiaga@qmul.ac.uk
a.aker@is.inf.uni-due.de
Kalina Bontcheva Maria Liakata, Rob Procter
University of Sheffield, UK University of Warwick and Alan Turing Institute, UK
k.bontcheva@sheffield.ac.uk {m.liakata,rob.procter}@warwick.ac.uk
with public opinion formation. Information disorder
has been categorised into three types [WD17]: (1) mis-
Abstract information, an honest mistake in information sharing,
(2) disinformation, deliberate spreading of inaccurate
This preface introduces the proceedings of the information, and (3) malinformation, accurate infor-
2nd International Workshop on Rumours and mation that is intended to harm others, such as leaks.
Deception in Social Media (RDSM’18), co-
located with CIKM 2018 in Turin, Italy. 2 Accepted papers
1 Introduction The workshop received 17 submissions from multiple
countries, of which 10 (58.9%) were ultimately ac-
Social media is an excellent resource for mining all cepted for inclusion in these proceedings and presen-
kinds of information, varying from opinions to actual tation at the workshop:
facts. However, not all information in social media
posts is reliable [ZAB+ 18] and thus their truth value • Kefato et al. [KSB+ 18] propose a fully network-
can often be questionable. One such category of in- agnostic approach called CaTS that models the
formation types is rumours where the veracity level is early spread of posts (i.e., cascades) as time series
not known at the time of posting. Some rumours are and predicts their virality.
true, but many of them are false, and the deliberate
fabrication and propagation of false rumours can be a • Caled and Silva [CS18] describe ongoing work on
powerful tool for the manipulation of public opinion. the creation of a multilingual rumour dataset on
It is therefore very important to be able to detect and football transfer news, FTR-18.
provide verification of false rumours before they spread
widely and influence public opinion. In this workshop • Yao and Hauptmann [YH18a] analyse the power
the aim is to bring together researchers and practition- of the crowd for checking the veracity of rumours,
ers interested in social media mining and analysis to which they formulate as a reviewer selection prob-
deal with the emerging issues of rumour veracity as- lem. Their work aims to find reliable reviewers for
sessment and their use in the manipulation of public a particular rumour.
opinion.
The 2nd edition of the RDSM workshop took place • Yang and Yu [YY18] propose a reinforcement
in Turin, Italy in October 2018, co-located with CIKM learning framework that aims to incorporate in-
2018. It was organised with the aim of focusing partic- terpersonal deception theories to fight against so-
ularly on online information disorder and its interplay cial engineering attacks.
Copyright © CIKM 2018 for the individual papers by the papers' • Conforti et al. [CPC18] propose a simple archi-
authors. Copyright © CIKM 2018 for the volume as a collection
tecture for stance detection based on conditional
encoding, carefully designed to model the internal
by its editors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC
BY 4.0).
structure of a news article and its relations with [KSB+ 18] Zekarias T. Kefato, Nasrullah Sheikh,
a claim. Leila Bahri, Amira Soliman, Alberto Mon-
tresor, and Sarunas Girdzijauskas. Cats:
• Roitero et al. [RDMS18] report on collecting Network-agnostic virality prediction model
truthfulness values (i) by means of crowdsourc- to aid rumour detection. In Proc. of 2nd
ing and (ii) using fine-grained scales. They collect RDSM, 2018.
truthfulness values using a bounded and discrete
scale with 100 levels as well as a magnitude esti- [PBP18] Endang Wahyu Pamungkas, Valerio
mation scale, which is unbounded, continuous and Basile, and Viviana Patti. Stance clas-
has infinite amount of levels. sification for rumour analysis in twitter:
Exploiting affective information and
• Skorniakov et al. [STZ18] describe an approach to conversation structure. In Proc. of 2nd
the detection of social bots using a stacking based RDSM, 2018.
ensemble, which exploits text and graph features.
[RDMS18] Kevin Roitero, Gianluca Demartini, Ste-
+
• Caetano et al. [CMC 18] investigate the public fano Mizzaro, and Damiano Spina. How
perception of WhatsApp through the lens of me- many truth levels? six? one hundred?
dia. They analyse two large datasets of news and even more? validating truthfulness of
show the kind of content that is being associated statements via crowdsourcing. In Proc. of
with WhatsApp in different regions of the world 2nd RDSM, 2018.
and over time. [STZ18] Kirill Skorniakov, Denis Turdakov, and
Andrey Zhabotinsky. Make social net-
• Pamungkas et al. [PBP18] describe an ap-
works clean again: Graph embedding and
proach to stance classification, which leverages
stacking classifiers for bot detection. In
conversation-based and affective-based features,
Proc. of 2nd RDSM, 2018.
covering different facets of affect.
[WD17] Claire Wardle and Hossein Derakhshan.
• Yao and Hauptmann [YH18b] analyse a publicly Information disorder: Toward an interdis-
available dataset of Russian trolls. They analyse ciplinary framework for research and poli-
tweeting patterns over time, revealing that these cymaking. Council of Europe report, DGI
accounts differ from traditional bots and raise new (2017), 9, 2017.
challenges for bot detection methods.
[YH18a] Jianan Yao and Alexander G. Hauptmann.
Reviewer selection for rumor checking on
Acknowledgments social media. In Proc. of 2nd RDSM, 2018.
We would like to thank the programme committee
members for their support. [YH18b] Jianan Yao and Alexander G. Hauptmann.
Temporal patterns of russian trolls: A case
study. In Proc. of 2nd RDSM, 2018.
References
[YY18] Grace Hui Yang and Yue Yu. Use of inter-
[CMC+ 18] Josemar Alves Caetano, Gabriel Magno, personal deception theory in counter social
Evandro Cunha, Wagner Meira Jr., Hum- engineering. In Proc. of 2nd RDSM, 2018.
berto T. Marques-Neto, and Virgilio
Almeida. Characterizing the public per- [ZAB+ 18] Arkaitz Zubiaga, Ahmet Aker, Kalina
ception of whatsapp through the lens of Bontcheva, Maria Liakata, and Rob Proc-
media. In Proc. of 2nd RDSM, 2018. ter. Detection and resolution of rumours in
social media: A survey. ACM Computing
[CPC18] Costanza Conforti, Mohammad Taher Surveys (CSUR), 51(2):32, 2018.
Pilehvar, and Nigel Collier. Modeling the
fake news challenge as a cross-level stance
detection task. In Proc. of 2nd RDSM,
2018.
[CS18] Danielle Caled and Mário J. Silva. Ftr-
18: Collecting rumours on football transfer
news. In Proc. of 2nd RDSM, 2018.