=Paper=
{{Paper
|id=Vol-2882/MediaEval_20_paper_58
|storemode=property
|title=You
Said
It? How Mis- and Disinformation Tweets Surrounding the Corona-5G-Conspiracy Communicate Through
Implying
|pdfUrl=https://ceur-ws.org/Vol-2882/paper58.pdf
|volume=Vol-2882
|authors=Lynn de Rijk
|dblpUrl=https://dblp.org/rec/conf/mediaeval/Rijk20
}}
==You
Said
It? How Mis- and Disinformation Tweets Surrounding the Corona-5G-Conspiracy Communicate Through
Implying==
https://ceur-ws.org/Vol-2882/paper58.pdf
You Said it? How Mis- and Disinformation Tweets Surrounding
the Corona-5G-conspiracy Communicate Through Implying
Lynn de Rijk, Radboud University, Netherlands
l.derijk@student.ru.nl
ABSTRACT 2 METHODS
This paper aims to investigate if implied meaning plays a role Using the dataset compiled for the MediaEval 2020 FakeNews
in mis-/disinformation tweets and what linguistic cues might Task [3], a qualitative analysis of 130 tweets was performed,
signal this. A qualitative analysis of 130 mis-/disinformation coding for direct and indirect SA’s in Atlas.ti (version 8.4.5).
tweets regarding the corona-5G-conspiracy using Speech Act The coding process was iterative and additional codes were
Theory, shows that often meaning is implied by leaving out added based on patterns found in the data, such as recurring
coherence markers, putting the words in someone else’s linguistic features, like coherence markers (e.g., ‘no
mouth through citing and ambiguous phrasing/punctuation. meaningful connectives’) and certain SA’s (e.g., ‘citing’). Some
codes were mutually exclusive (such as ‘Indirect SA: None’
1 INTRODUCTION with other Indirect SA’s), where other codes were not (e.g.,
Systems that automatically recognize mis-/disinformation are tweets were often coded for multiple direct SA’s, see Example
challenged by certain basic communicative features, such as 1). Only tweets supporting a conspiracy (e.g., corona is a
sarcasm or implied meaning. It is therefore relevant to find coverup for 5G deaths or 5G causes corona) were included in
out if indirect communication plays a role in mis- the result section, as tweets with a different stance were
/disinformation and how large this role is. Speech Act Theory rarely found (n = 7). Furthermore, only tweets that were not
[1] can be used as a framework to index implied meaning. It part of a thread were analyzed, to ensure that the context did
distinguishes three forces in every utterance: 1) locution, not differ in regard to one-to-many vs. one-to-one interaction.
what is said literally, 2) illocution, what the utterance does The data was coded iteratively until a saturation point was
and 3) perlocution, what happens as a result. For example, in: reached within these inclusion criteria (n = 90). The final code
“Peter, you are standing on my foot”, the locutionary force is list can be found in Appendix 1. Indirect SA’s were only coded
asserting this state of affairs. The illocution, would generally for when these were the primary communicative force. For
be requesting that this Peter lifts his foot, now that he is made example, the tweet below was coded for ‘Indirect SA:
aware. The perlocutionary act would then be that Peter concluding’, as the implied meaning is most likely the primary
indeed places his foot elsewhere. meaning. Without the implication the assertions made are
However, Micheal Geis argues that that the illocutionary force simply loose statements.
of a Speech Act (SA) is mostly dependent on the context, not Anyone else curious about the majority of deaths in china seem
linguistic cues [2]. Geis gives the example of a teacher asking a to be the same areas they rolled out their stand alone 5G just a
student if they can solve a quadratic equation vs. the same couple months ago. Verry few deaths being reported in other
question being asked by a fellow student. The first would areas in comparison. #5G #CoronavirusOutbreak #COVID19
count as a request for information (Do you need help?), where #5gamechanger (Example 1)
the second is more likely a request for action (I don’t
understand, help me.) The importance of context is what The user asks a question in the first sentence, evidenced by
makes recognizing illocutionary force difficult for an the syntactic structure of the sentence (‘direct SA: asking’),
automated system. even though they did not use punctuation. This is followed by
When dealing with data from a platform such as Twitter, two assertions (‘direct SA: asserting’). The intended relation
however, this becomes less of an issue, since in one-to-many between the three sentences is not made explicit through
communication every member of the audience is addressed coherence markers such as meaningful connectives (‘no
relatively equally and the general context is the same for each meaningful connectives’). The last sentence does have a lexical
tweet (Twitter). This exploratory study aims to find if indirect cue phrase (phrases that show the relation between sentences
SA’s play a role in mis-/disinformation tweets and if so, if or the attitude of the speaker, e.g., ‘in my opinion’ or, in this
there are linguistic features identifiable that can capture tweet, ‘in comparison’), that shows the relation between areas
indirect SA’s. the user wishes to point out. As the causal relation is not made
explicit, the act of concluding that these assertions are
causally related is an indirect SA.
3 RESULTS
Copyright 2020 for this paper by its authors. Use permitted under Users seem to employ a couple of strategies to avoid outright
Creative Commons License Attribution 4.0 International (CC BY 4.0). claiming there is a conspiracy, often communicating through
MediaEval’20, December 14-15 2020, Online
MediaEval’20, December 14-15 2020, Online M. Larson et al.
implication (see Table 2). First, they often omit connectives, 4 DISCUSSION
leaving the relation between sentences implicit (e.g., Example
This study found that there are linguistic cues identifiable that
1 and Table 3). Second, they tend to cite others (Table 2), such
capture indirect SA’s, such as omission of certain connectives
as citing the headline of an article they then link to, or use
and lexical cue phrases. A possible explanation for these
other means to put a middleman between themselves and
findings is that users might (subconsciously) try to
what is said, as can be seen in Example 2:
circumvent the forewarning effect. This effect has been studied
I’ve been reading a few posts from ppl I know about how the
extensively in psychology and suggests that forewarning is a
#CoronavirusOutbreak is because of 5G trials and Wuhan was
factor that causes resistance to persuasion [4]. Using
the place that first rolled this out and therefore is seen to be
connectives and lexical cue phrases helps reader
used as a biologocal warfare weapon... 😬😬 #coronavirus comprehension in informative texts, but in persuasive texts
(Example 2) can build up the reader’s resistance, since they recognize
In this tweet, connectives and lexical cue phrases (because of, more easily when they are being persuaded [4]. Apart from
therefore) are used to make author intent clear, but the user the omission of connectives and lexical cue phrases, the
puts the words in the mouths of ‘ppl I know’. ambiguity of certain tweets also points to this explanation.
Alternatively, the omission of certain words might also be a
Table 1: Frequency of most used direct SA’s result of the affordances [5] of Twitter, where the limited
Code (not mutually Number of tweets Percentage of the characters per tweet might incentivize users to leave out
exclusive) (n = 90) dataset words they deem unnecessary. However, this does not seem
Direct SA: to be the case, as one would expect that emojis would be used
- asking 28 32.2% quite often, since they leave room for ambiguity while
- asserting 69 76.7% simultaneously only taking up one character space. As seen in
- citing 24 26.6% Table 1, this is not the case. Additionally, emojis are often used
- describing 29 32.2% decoratively as well, such as arrows or bullet points, without
making use of their potential for ambiguity. Furthermore,
Table 2: Distribution of indirect SA’s (suggesting, citing linked article titles is an interesting practice with these
concluding, inviting, describing)
affordances in mind, as it leaves little room for the user’s own
Code Number of tweets Percentage of view. The limited space Twitter affords gives weight to the
(n=90) the dataset chosen citation, which in turn creates the implication that the
Indirect SA 59 65.6% citation is important/true/relevant.
No indirect SA 31 34.4% Lastly, leaving things ambiguous and citing others, could point
to a user orientation to distance themselves from conspiracy-
Table 3: Distribution of linguistic features thinking – a way for users to keep plausible deniability for
their support of what is said. It should be noted though, that
Code (not mutually Number of tweets Percentage of
grammatical or punctuation ambiguity might also be a result
exclusive) (n = 90) the dataset
of users not being native English-speakers or simply
No meaningful 52 57.7%
inattentiveness or oversight by the user.
connectives
Similar to [6]’s findings on Indonesian hoax data, I found that
Lexical cue phrases:
SA Theory can be useful to analyze mis-/disinformation online
- Opinion 26 28.9%
- Relation 19 21.1% data. Where they focused on direct SA’s, finding that assertive,
Emoji use 12 13.3% directive and expressive SA’s were most common. In this
study, indirect SA were also considered, showing that
communication is often indirect in mis-/disinformation
Third, users employ ambiguous phrasing. This can be seen in
tweets. Future research could compare these findings to non-
Example 1, where the question is not clearly stated, since a
conspiracy-tweets, to shed some light on which explanation
question mark is omitted and instead an assertion follows
provided here is more plausible and to show if the found
immediately. It is thus phrased initially as a question, but the
linguistic features might aid in distinguishing information
question itself does not seem of much importance. In Example
from mis-/disinformation. It would also be useful to see if
2, this strategy can also be seen in ‘😬😬’. It is left to the reader machine-learning might have already been able to distinguish
to infer what the target of the emoji is; is it the entire prior between the two regardless of picking up on the indirect SA’s.
statement (how awkward that people I know say these things)
or only the part describing a possible relation between 5G and ACKNOWLEDGMENTS
corona (there might be a relation between corona and 5G)? I thank Martha Larson for her input and critical eye. I also
Depending on the interpretation, the meaning of the tweet thank John Keates and Zhengyu Zhao for help with pre-
changes completely, even with regard to the user’s stance. processing.
FakeNews: Corona virus and 5G conspiracy L. de Rijk
REFERENCES
[1] J. R. Searle, 1965. What is a Speech Act? In M. Black (Ed.), Philosophy in
America, pp. 221-239. Allen and Unwin.
[2] M. L. Geis, 1995. Speech acts and conversational interaction. Cambridge
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[3] K. Pogorelov, D. T. Schroeder, L. Burchard, J. Moe, S. Brenner, P. Filkukova
and J. Langguth 2020. FakeNews: Corona Virus and 5G Conspiracy Task at
MediaEval 2020. In Working Notes Proceedings of the MediaEval 2020
Workshop.
[4] J. Kamalski, L. Lentz, T. Sanders and R. A. Zwaan, 2008. The Forewarning
Effect of Coherence Markers in Persuasive Discourse: Evidence from
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[6] A. A. Iswara and K. A. Bisena, 2020. Manipulation and Persuasion through
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