Fake News – what do real writers think about it? AI research

I examined in August 2020 research (UAL London see credits) what happens when writers use a computer text generator to write articles, giving them only an image prompt. There is a summary of the Study in the Index.

Go to Index of AI research

Fake News – (or ‘fake news’) what do real writers think about it?

Summary 

  • People who have a lower general opinion of text generation, and lower wordcounts for feedback, don’t think text generation is relevant to ‘fake news’.

The converse, that people who are more engaged with text generation (more feedback, more positive) can see that it is more related to fake text generation.

So, if people are educated on the topic, they will realise there is a relationship between advances in computer text simulation and fake news.

Definition of fake news

Even defining fake news is not that simple, since if you look for examples in the USA, or Syria, or anywhere else, both sides accuse the other of using it. Depends on there being no consensus as Barak Obama mentioned recently. See the bottom of the blog for some discussion.

Fake News

One of the feedback questions in the Study was about whether computer AI text generation was relevant to ‘fake news’ (false content for spam, propaganda etc.).

Q6-2: If you have heard of ‘fake news’ do you think this is relevant?

This had neutral phrasing in order not to influence replies. It did not say ‘text generator can make fake news’ as this presupposes a technical knowledge that might not be present, even after doing the experiments. Given the sometimes bizarre output of the generator, it might affect replies too specifically.

18 of the 82 respondents – 22% – replied to the question. It was the last question after a long session so perhaps some fatigue had set in. It was also a question not specifically to do with the experiment. About a quarter is still quite a high response rate.

Analysis of responses

I will be examining the actual text answers in another blog. This just looks at some basic relationships.

Several comparisons of the data were calculated and some are worth discussing here (the others are in reference material).

Comparing answer word count with positive – negative ratings:

The actual Q6 answer text was assessed for Positive – Neutral – Negative response to the ‘relevant to fake news’ question.

For instance, the text answer ‘No’ was 2 for negative, word count 1. ‘I stay out of these type debates.’ Is 1 for neutral, word count 7.

This was quite easy to do. I did not use a computer sentiment analyser as the samples were too small. Anything a bit ambiguous or with conflicting statements was rated Neutral.

Pos – neg is 0 (positive), 1 (neutral), 2 (negative) – these are scaled up so can be seen against the word count values. So low red columns are positive (yes, relevant to ‘fake news’) , high red is negative (not relevant to ‘fake news’). This is an ordinal scale.

fake news vs word count.png

Above: Pos-neg is zero for positive for ‘relevance’ to ‘fake news’. The red Pos-neg 0-1-2 value is scaled to make it easier to see. So a tall red line means negative for relevance to fake news. (There is a discussion of different statistical calculations in a forthcoming blog. The tests used in the study are Mann Whitney U, T-Tests, ANOVAS, and Pearsons, along with various bar charts and boxplots.)

At first glance this shows that higher answer word counts are associated with positive for ‘relevance to fake news’.
Low word counts are associated with negative for ‘relevance to fake news’.
There is only one negative out of the top half of the responses (1-9 on the chart above, 2-8 are pos=0).
The highest word count (column 1 on chart) was from an answer that discussed an actual example of real ‘fake news’, or propaganda in the Syrian War, so was a longer than usual negative response. It could be an outlier. After ruminating they decided it was too hard for a text generator to do, writing ‘news of any kind has to be created by humans…’ which is a value judgement (humans are best).

Statistics

Mann Whitney U test

The z-score is 4.25539. The p-value is < .00001. The result is significant at p < .05.

  • People who used the least words thought text generation was less relevant to ‘fake news’.
  • People who used more words in their answer thought it was more relevant to ‘fake news’.

Discussion

In other work, computer Sentiment Analysis revealed that all feedback answers had high scores for ‘Tentative’ and very low (no score) for ‘Confidence’. These results could be because of a general lack of experience of text generation (only 11% had previous experience).
There is a forthcoming blog on the Sentiment Analysis, there is already a blog on Sentiment Emotions (Joy etc.).

When making a positive claim (‘there is relevance’) with no ‘Confidence’ and feeling very ‘Tentative’, discursive answers are to be expected.

In the positive answers, the relevance of ‘fake news’ produced more discussion (‘Yes, and here’s why…’). This could be because the respondent felt it was relevant, but had little or no confidence in their feelings and supplied a tentative, more wordy, answer.

Whereas with a negative response, text generation is not relevant to ‘fake news’, and can be easily dismissed (‘No’ – 1 word).

Ranked Likert scores over the Study vs Pos-neg

Fake-ranked-Likert-Pos-neg.png
Mann-Whitney U Test
The z-score is 5.10963. The p-value is < .00001. The result is significant at p < .05.

This is not visually very clear until you sum the raw (before scaling for graph) scores from columns 1-8 (6) and 2-18 (10).

  • This shows that lower Likert scores (which means positive response to experiments and later questions) relates to lower Pos-neg scores (which means positive relevance to ‘fake news’). This is what would be expected.
  • People who have a lower general opinion of text generation, don’t think text generation is relevant to ‘fake news’ (and possibly anything else of practical use).

Amateur – Professional

People were asked to select Amateur/Professional (or both, scored as Pro) next to Occupations. Most people had more than one occupation (Poet and Journalist etc.) so it is not a fixed job allocation, but provides an indication that the writer either is or would like to be paid for at least some of their work.

This did not provide any significance at the ‘fake news’; response level.

There is a forthcoming blog on Amateur/Professional differences.

Comments

Is there a connection between text generation and ‘fake news’?

1/ Human pride: anthropocentrism: people that dismiss the use of text generation and its relevance to ‘fake news’, say ‘news of any kind has to be created by humans…’.

2/ The idea that a computer could not be used to write news, ‘fake’ or otherwise, might be due to the rather random output of the mid-level (GPT-2) text generator used in the Study. People might think it is producing readable nonsense, and brusquely dismiss any practical uses. This is shown in the relation between overall negative scores (Likerts) and low relevance for ‘fake news’.

3/ Perhaps dismissal of a text generation tool in regard to ‘fake news’ is indicative of not believing there is ‘fake news’ (or that it is not a problem), shown in the low word counts of their short negative responses. Further research required.

Notes

1/ Answers

If you have heard of ‘fake news’ do you think this is relevant?

Replies went from the four shortest (1 or 2 words)

If you have heard of ‘fake news’ do you think this is relevant?

No (scored 2, neg)

No! (scored 2, neg)

Sure (scored 0, pos)

Not really (scored 2, neg)

to the four longest (89, 85, 76, 50 words; 2,2,0,0)

If you have heard of ‘fake news’ do you think this is relevant?

“Unless the AI used to generate the text is extremely advanced, I think any news text generated wouldn’t be very effective — fake or otherwise. I think, at this point in our technology, news of any kind has to be created by humans to effectively educate, manipulate, or make humans react like chickens with their [thinking] heads cut off. If a text generator is going to be effective at writing propaganda, it’s skills are going to have to increase a heck of a lot. Ask again in five years.” (scored 2, neg)

“Very difficult to unpack – particularly as the different sides each claim the other side is distributing fake news. Having followed the Syrian War closely, I know for a fact our major news channels spouted fake news. Could something like this be used in social media to reply with biased opinions? Perhaps, but the existing troll farms used by all sides are often quite sophisticated, with the least sophisticated being the least effective. So, this type of low level autogeneration would not be very effective.” (scored 2, neg)

“Oh yes! Too many people are not analyzing the sources. Maybe that is too big a job or most and maybe many do not know to do this. My son repeats this claptrap to me as if it is gospel and he does not understand me when I tell him that his source is untrustworthy. He thinks if he hears something from dozens of people it must be true. Mass media is powerful in that way. “ (scored 0, pos)

“It is an enabler of fake news (eg allowing much faster fake news to be created – especially if fed in the analytics re effectiveness of dissemination etc.) but could also make REAL news reporting more efficient and effective especially if it supports optimum use of human vs AI input.” (scored 0, pos)

The text samples were too small to use computer sentiment analysis, so I scored the replies myself (human accuracy is still the best for nuanced material). Much training data for sentiment analysis is annotated by humans before use. See references.

Positive – is relevant – 0

Neutral – don’t know, don’t care or not sure – 1

Negative – is not relevant (not good enough, unsuitable, not required) – 2

How done

Sum Likerts (inverting the sixth Q (Q3/of 6 at end), the third after the 3 experiments), rank, then compare to texts, which are scored

0 pos (relevant to fake news), 1 neutral, 2 neg (not relevant to fake news).

Note: Q3/6: Do you feel that you have used somebody else’s work?

This Likert scored the other way, ie, the first option (score 1) was the most negative (is somebody else’s, ie plagiarism).

So in the calculations this score was inverted (1 become 5 etc.).

Other Calculations

1/
Overall Likert score (low positive affect) vs Fake Pos-neg relevant – neutral – not relevant

fake news vs likert.png

There is no significance to this relationship.

2/

vs. word count vs Fake news Pos-neg relevant – neutral – not relevant

fake news vs word count.png

References

Sentiment analysis papers:

https://www.kdnuggets.com/2020/06/5-essential-papers-sentiment-analysis.html

What is fake news?

Important researcher: Xinyi Zhou
These are the most recent papers on fake news:

A Survey of Fake News:

https://dl.acm.org/doi/10.1145/3395046

Fake News Early Detection: 

https://dl.acm.org/doi/10.1145/3377478

Generally, fake news is false or distorted information used as propaganda for immediate or long-term political gain. This definition separates it from advertising, which has similar approaches but is for brand promotion rather than life or death matters. False can be anything from an opinion to a website which appears to be proper news but is actually run to spread false information.

This has led to ‘reality checks’ where claims are checked against reality (which still exists) but the problem is that fake news spreads very quickly (because it appeals to the emotions, often fear or hate) while corrections take time to process and are very dull, so hardly anyone reads them, as they have no emotional content. Certainly the people that react to the fake news (if it is proven so) have moved on already, to the next item.

 

 

 

 

 

 

Festival UK 2020 for Creative Computing Institute University Arts London

CCI UAL Camberwell London UK
CCI UAL Camberwell London UK
There’s just been an announcement about the exciting #FestivalUK2022. Professor Grierson (my research director) is part of this, with Creative Computing Institute, CCI UAL. 30 creative teams have been selected to take part in the forthcoming festival of creativity in the R&D Project phase. #STEAM – science, technology, engineering, arts, mathematics.

More news on as this story develops.

The tweet is at
https://twitter.com/ual_cci/status/1328292551967854594

Writing occupation and emotions in text generation

In August 2020 research (UAL, see credits) I examined what would happen if and when writers use a computer text generator to write articles, giving them only an image prompt. The idea was to only use professional or serious amateur writers.

Go to Index of AI research

Joy, Fear, Anger, Sadness – emotion charts are after this introduction.

Can text generation help the human writing process? What do actual writers (the study respondents) think of it all?

The research examines creative and ethical concerns around the use of advanced systems, and how they will (or already do) affect stakeholders, both professional writers and serious amateurs.
Here’s the prompt image:

Prompt image man and dog
Prompt image man and dog

The results are in but I am still writing it up. So I am now dropping a few things on this blog. These are not the final results as many qualifiers need to be added, statistical definitions, significance, etc. There are over 50 charts, which is why the report is taking a long time.

More about boxplots: This is a blog about some study results boxplots. if you are not sure what it all means, please look at this first.

One question asked was whether they’d used a text generator before, someone replied ‘my unconscious’. 89% had never used a text generator before.

82 respondents from my own creativity writing app list (see below), and various professional bodies.

These are Occupation (type of writer eg, Student, Poet, Journalist etc. – see the left axis);
plotted against amount of Emotion (joy, anger etc.) in their written feedback to all the questions (summed, then scored using a sentiment analyser). (Amateur and Professional are not attached to the actual occupation, so they are on here too.)

Increased emotion values towards the right side of the chart. These plots show ranges so they only give a general visualisation.

Joy

So in the boxplot below, the most joy in responses came from Copywriters.

Perhaps they see a fantastic tool to very quickly make more copy.

Joy vs Occupation
Joy vs Occupation

Fear

The most fear in responses came from Poets and Fiction writers. Perhaps fear of losing their respect as creators of strange new worlds were no one has gone before. Or they see a fantastic tool to very quickly make them unemployed. Other and Scribbler also score on this emotion.

Fear vs Occupation
Fear vs Occupation

Anger

Would appear that Others and Scribblers are somewhat angry about something or other. More research needed! Poet and Fiction also score highly, one each here (a line).

Anger vs Occupation
Anger vs Occupation

Sadness

Perhaps poets know more sad words.

Sadness vs Occupation
Sadness vs Occupation

There’s lots more charts but that will do for today. The actual stats with significance, etc., are for future viewing.

One of the simple charts:
Time Average on Study by Occupation

Graph- Time Rank Occupations
Graph- Time Rank Occupations

Game writers had 2 outliers, one person was on it for hours. Perhaps text generation is familiar to games content writers as some games have generated scenarios. Or they have a lot of spare time – to play games.

(Possibly) confirms rumour that songs are written quickly, and that lyricists and poets have flashes of inspiration quickly recorded (and so do copywriters and scientists). Or they were in a hurry to get away…
Game and Songs, Lyrics were added by people within Other definition.

Next blog – the text generation itself.
In the experiment, people were advised to use the generator to make completed works. Several people put my name in the generator, so I became the protagonist in the stories. What!

Such as this Fiction entry:
“It was nice to hear from Geoff again. He is a reminder that life is like an ant’s journey on a blade of grass across a puddle. There is no other side to reach, because the ant is surrounded on all sides. Like an ant, like all of us, Geoff has strategies for paddling. One admires only the paddling, and not especially the termination of the journey. And perhaps that’s what should be the focus of our lives: the paddling. Not journey, not the conclusion, but the sheer determination of the paddling. With a surfer, this analogy would not work, but thinking about it, ants can’t surf.”


People used the OpenAI GPT-2 text generator in a two panel design. I’m releasing this setup as a free AI text editor soon. The generator version is Text Synth by Fabrice Bellard, who is very helpful.

University of the Arts London: my tutor at UAL CCI is Professor Mick Grierson. See Credits (new window). My app is Notes Story Board, an image and text zooming canvas.

Computer-Human Hybrid AI Writing and Creative Ethics

Introduction

This blog is about my 2020 research into computer text generation and the effects on professional ands amateur writers. I am working on this topic at the University of the Arts London (UAL CCI, Dir. Mick Grierson).

No-one has asked creatives or writers what they think of the new ‘AI’ systems that generate readable text and so directly threaten their jobs, and could change the way people work forever (or don’t work forever). This is a topic that directly impinges on self-worth and financial worth in more ways than anyone can imagine, although plenty are worrying.

STUDY – ONLINE EXPERIMENT
August-October 2020

I devised an online experiment about this topic, allowing respondents to experiment with creating hybrid stories using a text generator. The people were all professional or serious amateurs (and a couple of small students) invited from my own creative writing software mailing list, a couple of writing forums, and a publisher’s writers’ forum, plus friends and relatives who generally use writing in their work. Credits are at the bottom.

Text generation

You might have heard of Google OpenAI’s GPT-2 and GPT-3. My experiment uses a generating system (Fabrice Bellard’s Text Synth, with permission)  based on GPT-2, that anyone can use. GPT-2 was used here as the model works well for idea generation and is more generally available at the time than GPT-3, which is much larger.

Note: The text generation and editing system is now a free online tool (creativity support tool or CST) at

Story Live writing with AI free online

The experimental results will feed into this blog (see Index for different aspects) and later an academic paper, and also a new book for the general public on the whole subject of computers, creativity and writing.

Please sign up for news and notifications – there’s a form on this page.

Brief description of the Study

Below is a graphic of the entire online study. Each block is a page and journey was left to right from top to bottom. The three text generation and editing experiments used a similar set up to the Story Live tool.

Each writing experiment – Caption, News and Fiction – had a question afterwards, then there were more questions after the experiments (see diagram below). All this will be addressed in blogs here, along with other discussions.

The image writing prompt was the same for each experiment and for all respondents for uniformity (there is a blog on the man and dog here).

Prompt image man and dog
Prompt image man and dog
Flowchart of Study

Geoff Davis

The computer support tool (CST) from this study is Story Live writing with AI free online

My other creativity tools are Notes Story Board and Story Lite from my Story Software. For my other activities please see the home page of this site.

Study

This study was devised and the site programmed by Geoff Davis for post-graduate research at University of London Creative Computing Institute UAL CCI 2020. The Supervisor is Professor Mick Grierson, Research Leader, UAL Creative Computing Institute.

Text Synth

Text Synth, by Fabrice Bellard, is a publicly available text generator, was used as this is the sort of system people might use outside of the study. It was also not practical to recreate (program, train, fine-tune, host) a large scale text generation system for this usability pre-study. Permission was granted to use Text Synth in the study by Fabrice Bellard Jul 7 2020.

Fabrice Bellard, coder of Text Synth.
Fabrice is an all-round genius and writes a lot of OS. Text Synth was built using the GPT-2 language model released by Google OpenAI. It is a neural network of 1.5 billion parameters based on the Transformer architecture.