Persuasive Language and Big Data

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Marco Guerini

INTRODUCTION CONSENSUS AND SPREADING

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Premise

Information is vital, it creates emotions, moves ideas, brings people to act.

By analyzing social and linguistic dynamics, we want to understand how to build consensus and promote spreading of information within a given context.

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Premise By means of a series of examples, I will show some of the characteristics that linguistic communication must have to be effective.

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Approach •  Automatic analysis and recognition of the

persuasive impact of communication.

•  Address the various effects which persuasive communication can have in different contexts on different audiences.

•  Focus on the analysis of big corpora specifically developed for the task.

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Persuasive Corpora •  Corpus, -digital- collection of texts from a

specific author, on a given topic, of a given type.

•  Linguistic data should be possibly augmented with annotation of various audience reactions and metadata.

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2 Examples of Corpora •  CONSENSUS - Political speeches tagged

with audience reactions.

•  SPREADING - Post on Social Networks annotated with I_like, comments, etc.

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Consensus Indicators

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•  Positive-Focus: a persuasive attempt that sets a positive focus in the audience. Tags considered:

{APPLAUSE} , {STANDING-OVATION} , {SUSTAINED-APPLAUSE} , {CHEERING} , etc.

•  Negative-Focus: a persuasive attempt that sets a negative focus in the audience. Negative focus set towards the object of the speech not on the speaker.

{BOOING} , {AUDIENCE} No! {/AUDIENCE}

•  Ironical: Indicate the use of ironical devices in persuasion. Tags considered:

{LAUGHTER} and multiple tags containing laughter.

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Freedom has many difficulties and democracy is not perfect. But we have never had to put a wall up to keep our people in, to prevent them from leaving us. {APPLAUSE ; CHEERS} I want to say on behalf of my countrymen who live many miles away on the other side of the Atlantic, who are far distant from you, that they take the greatest pride, that they have been able to share with you, even from a distance, the story of the last 18 years. I know of no town, no city, that has been besieged for 18 years that still lives with the vitality and the force, and the hope, and the determination of the city of West Berlin. {APPLAUSE ; CHEERS}

White buzz positive comments. “The best product I have ever bought” Black buzz negative comments. “Do not buy this product, it is a rip-off” Raising discussion the ability to induce discussion among users Controversiality polarize the audience (pro or against the given content) Fostering Elaboration induce to elaborate on the given content …

Spreading Indicators

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Post Text Like ReShare Comments

Consensus is about the subtle art of saying the right thing at the right moment.

1218 54 360

Consensus is about the art of knowing what to say.

2 0 5

… … … …

Methodology •  Classical approaches based on the study

of text characteristics. Simple count of key-words in the document or analysis of its linguistic structures.

•  By means of specific mathematical formulae we can define the persuasive impact of linguistic material (words or structures that get a lot of applauses, reshare, etc.)

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Words used Topics Linguistic Style Readability Difficulty Rhetorical Structure …

Text Characteristics

THE BIG PICTURE CONSENSUS AND SPREADING

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Who delivers the content. What the content “says”. How it is said. When it was delivered. Where it was delivered.

WHO WHAT WHERE

5 Elements

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WHEN

HOW

An Example

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How is it possible that this video hit 1 billion views - in only five months - on YouTube???

If you want check: GANGNAM STYLE

Consensus

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The new internet earworm. Absolutely terrific!

Spreading

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WHO DELIVERS THE MESSAGE?

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Hey guys, check this out! We’ve been dancing all night at the White House!

Standard Approaches

•  Based on the study of WHO.

•  “Easy” to model by means of graphs where nodes represent users.

•  Some nodes have interesting properties.

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Standard Approaches

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Standard Approaches

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Language and Role •  Opinion leaders have a particular

language style that characterize them.

[Quercia et al. “In the mood for being influential on Twitter”]

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We can ident i fy those who can potentially draw a crowd, within a group, by analysing their language.

Female’s rhetoric far less aggressive than male’s - negative-focus tags density 60 times higher.

Language and Gender

25 0%

10%

20%

30%

40%

50%

60%

70%

Male Female

Carefully choose who shall deliver the communication according to context.

[Guerini et al. “The New Release of CORPS”]

WHEN IS IT BETTER TO DELIVER THE MESSAGE?

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Time •  Best time for posting on Twitter: from 9

a.m. to 13 p.m. •  Higher CTR: mid morning and early

afternoon •  Higher reshare: late afternoon

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Time

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It is better to deliver a content when users are highly receptive. Pay attention to the effect you want to achieve (only reads or in-depth analysis).

0 5

10 15 20 25 30 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Morning. Reads Evening. In-depth analysis

Language and Events Events that split the timeline in a before and after can be relevant for persuasive language.

The word “war” used 5 times more by G. W. Bush after 9/11. But, while before 9/11 it was widely used to get applauses, after it never got an applause.

29 Before After

Persuas

Persuas

Freq

Freq

Specific events can lead a good communicator to change, not as much his/her words, rather their rhetorical/persuasive use.

WHAT DOES THE CONTENT SAY?

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High Level Characteristics

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Today I’m very happy. Even if I have few days off, the sea and landscapes are stunning. Hiking away from the damned work…

Text only

High Level Characteristics

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Text + Pic

Trivially: more effective


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This holds true for a post, but also for an e-mail, a presentation, etc. Graphical and pictorial information grab users’ attention.

HOW TO SAY IT?

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Affective Words

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Usually a text with an affective load spreads more.


Affective Language Positive language is more viral than negative one (anger and fear are viral, but not sadness). What really matters is affective arousal (joy, anger and fear have high arousal, while sadness has a low arousal). [Berger and Milkman “Social Transmission, Emotion, and the Virality of Online Content”]

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How to convey negative news without getting others down?

Readability and Difficulty

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Thus "phenomenology" means -- to let that which shows itself be seen from itself in the very way in which it shows itself from itself. (Martin Heidegger, Being and Time)

Text difficulty - Example Scientific articles and readability. Only content should matter, nonetheless:

•  Bookmarked+ harder to read - Fogg-index = 21.1 •  Downloaded+ easier to read - Fogg-index = 18.2

[Guerini et al. “Do Linguistic Style and Readability of Scientific Abstracts Affect their Virality?”] 38

A text that is easy to read brings about an immediate action, a text hard to read

induces people to procrastinate…

Coarse Language

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Che figata, il mare é stupendo e i paesaggi commoventi. Solo un imbecille tornerebbe al lavoro.

Using vulgar expression does not necessarily bring about negative

reactions...

Today I’m very happy. Even if I have few days off, the sea and landscapes are stunning… BTW work sucks!!

Coarse Language and Consensus •  Surprisingly, coarse language used in posts with

lots of comments or likes (coverage 1.2), but not in controversial posts (coverage 0.9).

[Strapparava et al. “Persuasive Language and Virality in Social Networks”]

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You can actually use coarse language to obtain positive reactions…

Irony and Simple Language •  Reagan - aka the “great communicator” -

used irony (laughter density three times higher as compared to other speakers)

•  Reagan used a simple language: his persuasive words (and only those) polisemy degree is double.

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Irony and simple language can be used as an instrument for consensus.

Conclusions •  To understand how content can catalyze

consensus and spread, we need to study the who, what and how.

•  Focus on the analysis of big corpora specifically developed for the task.

•  A series of examples revealed specific characteristics of effective linguistic communication.

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References •  Berger J.A. and Milkman K.L. (2009) Social Transmission, Emotion, and

the Virality of Online Content. Social Science Research Network Working Paper Series.

•  Guerini M., Strapparava C. and Ozbal G. (2011) Exploring text virality in social networks. In Proc. of ICWSM-11.

•  Guerini M., Pepe A. and Lepri B. (2012) Do linguistic style and readability of scientific abstracts affect their virality? Proceedings of ICWSM-12.

•  Guerini M., Strapparava C. and Stock O. (2008) CORPS: A Corpus of Tagged Political Speeches for Persuasive Communication Processing. Journal of Information Technology & Politics, 5(1):19-32.

•  Guerini M., Giampiccolo D., Moretti G., Sprugnoli R. and Strapparava C. The New Release of CORPS: a Corpus of Political Speeches Annotated with Audience Reaction. Forthcoming.

•  Quercia D., Ellis J., Capra L. and Crowcroft J. (2011) In the mood for being influential on twitter. Proceedings of IEEE SocialCom-11.

•  Strapparava C., Guerini M. and Ozbal G. (2011) Persuasive language and virality in social networks. Affective Computing and Intelligent Interaction, 357-366.

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THANKS!

[email protected] www.marcoguerini.eu

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Persuasive Language and Big Data