Exploiting context for rumour detection in social media

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Presentation at Social Informatics 2017, Oxford, UK - 15th September, 2017

Exploiting context for rumour detection in social media
Arkaitz Zubiaga, Maria Liakata, Rob Procter
9th International Conference on Social Informatics (SocInfo 2017)
15th
September 2017

What IS a rumour?

What IS NOT a rumour?
●
There is confusion in the CS/CSS literature,
with many classifying rumour (false) vs
news (true).
●
Rumour not necessarily fake, it’s not the
same as a hoax!

What IS a rumour?
Oxford English Dictionary:
A currently circulating story or report of
uncertain or doubtful truth.
e.g. ‘they were investigating rumours of a
massacre’

What IS a rumour?
●
In line with other scientists since (Allport &
Postman, 1946):
“unverified and instrumentally relevant
information statements in circulation”
(Di Fonzo & Bordia, 2007)

So… rumour detection?
●
Rumour detection consists in identifying
the parts of information that, while in
circulation, are unverified.
●
We do this for breaking news stories.

Manual annotation of rumours
Performed by journalism partners.

Rumour dataset

Automated rumour detection
●
Only work by (Zhao et al., 2015). Looking at
how others reply, e.g. is this true?
●
Limitations:
– Regexps, not generalisable.
– Replies may delay or never come.

Automated rumour detection

Our proposal
●
Classify in real-time as soon as we observe
a tweet, don’t wait for replies.
●
Caveat: Tweet may not say much, e.g.:
“Strange flag in #Sydney, not the one affiliated with #ISIS.”
“10 people have died”

Our proposal
●
Look at context:
– What has been said before throughout the event?
– How do those help determine if current tweet is a
rumour?

Our proposal

Our proposal
●
We leverage context using CRF.

Results

Results by event

Discussion
●
Preceding context boosts real-time rumour
detection.
●
Important to do it in real-time, prevent
rumour spreading!
●
Rumour detection ≠ hoax detection!
– Nor fake news detection.

Questions?
●
Further reading:
Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, Rob Procter. Detection
and Resolution of Rumours in Social Media: A Survey. ACM Computing Surveys.
Forthcoming, pre-print at:
https://arxiv.org/abs/1704.00656
●
Dataset:
https://bitly.com/rumour-data
Contact: a.zubiaga@warwick.ac.uk, Twitter: @arkaitz

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We explore different scenarios to explain the chemical difference found in the remarkable giant-giant binary system HD138202+CD−3012303. For the first time, we suggest how to distinguish these scenarios by taking advantage of the extensive convective envelopes of giant stars. Methods. We carried out a high-precision determination of stellar parameters and abundances by applying a full line-by-line differential analysis on GHOST high-resolution spectra. We used the FUNDPAR program with ATLAS12 model atmospheres and specific opacities calculated for an arbitrary composition through a doubly iterated method. Physical parameters were estimated with the isochrones package and evolutionary tracks were calculated via MIST models. Results. We found a significant chemical difference between the two stars (∆[Fe/H]∼0.08dex), which is largely unexpected considering the insensitivity of giant stars to planetary ingestion and diffusion effects. We tested the possibility of engulfment events by using several different combinations of stellar mass, ingested mass, metallicity of the engulfed object and different convective envelopes. However, the planetary ingestion scenario does not seem to explain the observed differences. For the first time, we distinguished the source of chemical differences using a giant-giant binary system. By ruling out other possible scenarios such as planet formation and evolutionary effects between the two stars, we suggest that primordial inhomogeneities might explain the observed differences. This remarkable result implies that the metallicity differences that were observed in at least some main-sequence binary systems might be related to primordial inhomogeneities rather than engulfment events. We also discuss the important implications of finding primordial inhomogeneities, which affect chemical tagging and other fields such as planet formation. We strongly encourage the use of giantgiant pairs. They are a relevant complement to main-sequence pairs for determining the origin of the observed chemical differences in multiple systems.

Disentangling the origin of chemical differences using GHOST

Disentangling the origin of chemical differences using GHOST

Disentangling the origin of chemical differences using GHOST

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Isotopic evidence of long-lived volcanism on Io

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Isotopic evidence of long-lived volcanism on Io

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Disentangling the origin of chemical differences using GHOST

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Exploiting context for rumour detection in social media

1. Exploiting context for rumour detection in social media Arkaitz Zubiaga, Maria Liakata, Rob Procter 9th International Conference on Social Informatics (SocInfo 2017) 15th September 2017

2. What IS a rumour?

3. What IS NOT a rumour? ● There is confusion in the CS/CSS literature, with many classifying rumour (false) vs news (true). ● Rumour not necessarily fake, it’s not the same as a hoax!

4. What IS a rumour? Oxford English Dictionary: A currently circulating story or report of uncertain or doubtful truth. e.g. ‘they were investigating rumours of a massacre’

5. What IS a rumour? ● In line with other scientists since (Allport & Postman, 1946): “unverified and instrumentally relevant information statements in circulation” (Di Fonzo & Bordia, 2007)

6. So… rumour detection? ● Rumour detection consists in identifying the parts of information that, while in circulation, are unverified. ● We do this for breaking news stories.

7. Manual annotation of rumours Performed by journalism partners.

8. Rumour dataset

9. Automated rumour detection ● Only work by (Zhao et al., 2015). Looking at how others reply, e.g. is this true? ● Limitations: – Regexps, not generalisable. – Replies may delay or never come.

10. Automated rumour detection

11. Our proposal ● Classify in real-time as soon as we observe a tweet, don’t wait for replies. ● Caveat: Tweet may not say much, e.g.: “Strange flag in #Sydney, not the one affiliated with #ISIS.” “10 people have died”

12. Our proposal ● Look at context: – What has been said before throughout the event? – How do those help determine if current tweet is a rumour?

13. Our proposal

14. Our proposal ● We leverage context using CRF.

16. Results by event

17. Discussion ● Preceding context boosts real-time rumour detection. ● Important to do it in real-time, prevent rumour spreading! ● Rumour detection ≠ hoax detection! – Nor fake news detection.

18. Questions? ● Further reading: Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, Rob Procter. Detection and Resolution of Rumours in Social Media: A Survey. ACM Computing Surveys. Forthcoming, pre-print at: https://arxiv.org/abs/1704.00656 ● Dataset: https://bitly.com/rumour-data Contact: a.zubiaga@warwick.ac.uk, Twitter: @arkaitz