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- 1. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Session 7: narratives Disinformation/Malign Influence Training Disarm Foundation | 2022 1
- 2. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Course structure Understand environment 1: Introduction 2: Information and response 3: Threats Set up project 4: Project setup 5: Data collection Manage components 6: Influence 7: Narratives 8: Behaviours Manage risk 9: Risk assessment 10: Risk measurement Hotwash Module 7: Narratives ● Defining narratives ● Measuring narratives ● Narrative models ● Narrative counters ● Narrative tools ● Text analysis Exercise: narrative lists 2
- 6. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Narratives Narratives are what you believe; stories are how you communicate that. ● Stories don’t have to be true or believed. They can be used as signals ● We tell ourselves stories about ○ who we are and want to be (identity narratives); ○ who we belong to and don’t belong to (in-groups and out-groups); ○ what our world is, and ○ what is happening around us. Narratives define us as individuals and groups: families, communities, nations. 6
- 7. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Strategic narrative components: MICS Meaning Identity Content Structure Maan&Cobaugh, Introduction to Narrative Warfare
- 8. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Stories carry meaning, not truth “the currency of story is not truth, but meaning. That is, what makes a story powerful is not necessarily facts, but how the story creates meaning in the hearts and minds of the listeners. Therefore, the obstacle to convincing people is often not what they don’t yet know but actually what they already do know. In other words, people’s existing assumptions and beliefs can act as narrative filters to prevent them from hearing social change messages.” - Beautiful Trouble 8
- 9. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Targeting Who is a narrative targeting? ● This question works for information creators, disinformation creators, and responders Image: https://www.jcs.mil/Portals/36/Documents/Doctrine/pubs/jp3_13.pdf 9
- 10. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Potential Audiences ● Hardcore believers ● people whose identities are connected to showing that they believe a group narrative ● Persuadable: looking for information, usually related to concerns they have (or are raised by disinformation). ● Hardcore disbelievers 10
- 11. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Narrative timelines This timeline is for Covid19 ● What’s the election equivalent? ● Conflict example? ● Disaster example? Image: Kathleen Carley, a political disinfodemic 11
- 13. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Storytelling framework: story journey 13 Image: https://www.nationalgeographic.org/media/elements-storytelling/
- 14. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Storytelling framework: the hero’s journey 14 Image: https://www.peterfisk.com/2020/02/the-best-storytelling-frameworks-for-business-from-pyramid-thinking-to-the-heros-journey-steve-jobs-iphone-speech-and-mlks-i-have-a-dream/
- 15. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Sales funnels are stories too ● Image: https://suzanneperkinsm.medium.com/what-is-a-sales-funnel-and-why-websites-dont-work-anymore-1450fc99e69b 15
- 16. Disinformation/Malign Influence Training, Disarm Foundation | 2022 How narratives fit in: some pyramids 16
- 17. Disinformation/Malign Influence Training, Disarm Foundation | 2022 A messy image of who’s involved 17
- 18. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Lindholm: Story stickiness ● Simple, ● Unexpected, ● Concrete, ● Credible, ● Emotional, ● Stories 18
- 19. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Measuring narratives Think about the risk…
- 20. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Marketing: Brand Metrics ● Share of Voice - how much this brand is mentioned relative to competing brands. ● Consumer engagement ● Net sentiment (against competitor brands), and co-occurrence of brand with specific positive concepts (“brand health”), across consumers and influencers (and the deltas between these). ● Volume (against competitor brands) ● Message uptake ● Issue tracking - how much this brand is mentioned in conjunction with other narratives, and the themes inside those narratives. ● Relative visibility of brand executives (CEO etc), both relative to each other, and to executives from competing brands. ● Posts, reach, and engagement of mentions by influencers (journalists etc). ○ paid media is bought by the brand (paid search optimisation, advertising online, in print, TV, direct mail, affiliate marketing), ○ owned media is controlled by the brand (own websites, blogs, mobile apps, social media accounts, brochures, stores), ○ earned media is publicity generated through PR (targeting influencers, creating word of mouth discussion through engaging in social media, community conversations, blogs, and other user-generated content, brand advocates, and viral marketing in these spaces). ● The simplest sets of brand measures are counts of brand mentions, news mentions, social mentions, and engagement, and the changes in these over time. 20
- 21. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Basics: election example ● What: rumours from people’s uncertainties. ○ based on truth and community stories, unknown untruths, community signals, false information, false contexts, existing narratives. ● Who: people looking for information, political operators, countries ● When: before, during, after events ● How: seeded across user-generated content sites, nation state-controlled media, influencers ● Why: Goals include boosting one candidate/party, reducing support for other candidates/parties, creating panic, preventing people from voting, reducing trust in the election system, etc. 21
- 23. Disinformation/Malign Influence Training, Disarm Foundation | 2022 4D Model of disinformation campaigns (Nimmo) Dismiss: if you don’t like what your critics say, insult them. Distort: if you don’t like the facts, twist them. Distract: if you’re accused of something, accuse someone else of the same thing. Dismay: if you don’t like what someone else is planning, try to scare them off.
- 24. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Nimmo’s 4 Ds, plus 1 Distort Dismiss Dismay Divide Distract
- 25. Disinformation/Malign Influence Training, Disarm Foundation | 2022 BEND framework Maps the types of narrative manipulation used (“narrative TTPs”) Extends Nimmo’s “4 Ds” Image: Carley, social cybersecurity, an emerging science 25 https://www.armyupress.army.mil/Journals/Military-Review/English-Edition-Archives/Mar-Apr-2019/117-Cybersecurity/b/
- 26. Disinformation/Malign Influence Training, Disarm Foundation | 2022 FLICC John Cook Denial tactics: ● Fake experts ● Logical fallacies ● Impossible expectations ● Cherry picking ● Conspiracy theories Originally designed for climate change, HIV/AIDs etc crossover
- 28. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Debunking Corrects facts ● Trust matters ● Reach matters ● Intent of original posting matters ○ Add new narratives ○ Add belonging signals ○ Remove trust from individual / organisation ○ Remove trust in election process Image: https://firstdraftnews.org/articles/a-guide-to-prebunking-a-promising-way-to-ino culate-against-misinformation/ 28
- 29. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Injecting information Segmenting targeted population Using existing tools to inject clear messaging into same spaces as disinformation Build up a library of “cards” ready to use Image: https://realityteam.org/ 29
- 30. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Lakoff’s truth sandwich ● Truth ● Lie ● Truth 30 Image: https://www.americanpressinstitute.org/wp-content/uploads/2019/12/Sandwich-example.png
- 31. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Prebunking “Inoculates” people against disinfo FirstDraft lists 3 types: ● fact-based: correcting a specific false claim or narrative ● logic-based: explaining tactics used to manipulate ● source-based: pointing out bad sources of information Image: https://firstdraftnews.org/articles/a-guide-to-prebunking-a-promising-way-to-inoculate-against-misinformation/ 31
- 34. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Engage: Dennett’s 4 points 1. Attempt to re-express your target’s position so clearly, vividly and fairly that your target says: “Thanks, I wish I’d thought of putting it that way.” 2. List any points of agreement (especially if they are not matters of general or widespread agreement). 3. Mention anything you have learned from your target. 4. Only then are you permitted to say so much as a word of rebuttal or criticism.
- 35. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Mass-scale engagement: BuildUp and others https://howtobuildup.org/programs/digital-conflict/the-commons-project/ and https://americatalks.us/
- 37. Disinformation/Malign Influence Training, Disarm Foundation | 2022 List of popular narratives and counters 37 For elections, these include: ● Election date and location changes ● Scandals around politicians ● An election being won by a different candidate to the one declared winner. Work with your communities to create this list ● You will likely have top-level narratives with subnarratives, and narratives linked to different stages of an election ● List against prebunk/debunk
- 38. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Story tiles / image database Pre-populated images and simple text for common election-related misinformation (and other issues) 38
- 42. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Narrative campaign field guide https://www.narrative-strategies.com/ncfg
- 43. Disinformation/Malign Influence Training, Disarm Foundation | 2022 NCFG catechism Situation: ● 1. situation or opportunity the narrative campaign is being conducted in response to or in preparation for? ● Mission: ● 2. desired end-state of the campaign? Approach: ● 3. relevant target audience? ● 4. attitudes, behaviors, conclusions, and decisions within target audience to be influenced or generated? ● 5. sources and information channels considered most credible by the target audience? Why? ● 6. medium(s) and format(s) target audience most familiar with? ● 7. adversarial campaigns? ● 8. messages target audience already being exposed to? ● 9. methods used to influence target audience? ● 10. resources necessary to carry out this approach? ● 11. desired end-state of campaign? How is end-state measurable? Milestones: ● 1. metrics used to indicate success of campaign? ● 2. milestones that indicate progress toward desired end-state? 43
- 45. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Content analysis Set up ● Setting up notebooks ● Keeping track of what you’ve found Content analysis ● Techniques and tools for assessing misinformation artifacts: images, text, accounts, groups, domains etc. ● Text analysis
- 46. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Text Processing Information retrieval ● Search ● Named entity recognition Learning ● Classification ● Clustering ● Topic identification/ topic following ● Sentiment analysis ● Network analysis (words, people etc) Comprehension ● Natural language processing ● Translation ● Truthiness ● Gisting Creation ● Text generation
- 47. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Data sources Anywhere there’s text, e.g. ● Social media: Twitter, Facebook, Reddit, Youtube, Instagram, Medium etc ● Websites: https://commoncrawl.org/ extracts and saves text ● Wikipedia / Wikidata ● Documents
- 48. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Common Tools Standalone Tools: ● GPT2 / GPT3 ● Weka Python libraries: ● Scikit-Learn ● NLTK ● Gensim ● Spacy
- 49. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Text as “bags of words” ● Sentences: ○ “Oh really?”, ○ ”Just like with radiation poisoning then.” ● Words: “just”, “like”, “with”, etc ● Trigrams: “jus”, “ust”, “st “, “t l”, “ li”, “lik” “ike” ● Bigrams: “just like”, “like with”, “with radiation”, “radiation poisoning” ● Stopwords: “with”, “on”, “then”, “what”, “have”, “they”, “been”, “out”, “in”, “the”, etc "RT @KateShemirani: Oh really? Just like with radiation poisoning then. Put enough symptoms down on the diagnosis sheet and you can just abo… RT @Walletwalking1: @Sterling2143 @AAureilus Anyone noticed COVID symptoms are same as 5G exposure. What have they been rolling out in the… RT @ADDiane: Let's tell the people who won't wear masks that it's not for covid, it's for tricking the facial recognition software that dee… Discourse "
- 50. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Word Importance: Named Entity Recognition Finds names of people, organisations, locations etc in text Can use to create social graphs import spacy nlp = spacy.load('en_core_web_sm') sentence = "Bill Gates is selling 5G Covid19 data to Microsoft" doc = nlp(sentence) for ent in doc.ents: print(ent.text, ent.label_) Bill Gates PERSON 5 CARDINAL Microsoft ORG
- 51. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Sentiment: (some of) the feels ● Word-based: give (some) words positive/negative scores ○ Use an existing ‘sentiment dictionary’ ○ Score some words, use machine learning on the rest ● Document-based: score documents and use machine learning ○ ‘positive’/’negative’ for each sentence ● Semantic/pragmatic: use natural language processing ○ Satire is hard to detect ○ “Nice work bro!” ○ Emoticons are a language too Sentiment dictionaries: ● Wordstat: https://provalisresearch.com/products/content-analysis-software/wordstat-dictionary/sentiment-dictionaries/ ● Sentiwordnet: https://github.com/aesuli/SentiWordNet ● Emoticon sentiment lexicon: http://people.few.eur.nl/hogenboom/files/EmoticonSentimentLexicon.zip Very positive Positive Neutral Negative Very negative
- 52. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Example: Emilio Ferrara on Covid19 disinfo https://firstmonday.org/ojs/index.php/fm/article/view/10633/9548
- 53. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Generating to understand: Fireeye and GPT2 Image: Lee Foster, Phil Tully, Sajidur Rahman https://www.fireeye.com/blog/threat-research/2019/11/combatting-social-media-information-operations-neural-language-models.html
- 55. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Create a narrative list Lots of mindmapping tools out there ● We tend to use Miro
- 56. Disinformation/Malign Influence Training, Disarm Foundation | 2022 List out narrative TTPs and counters Use the NCFG for this List expected effects of counters List why to use those counters List why *not* to use those counters
- 57. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Bonus: find themes in your set of tweets Get the text from tweets into a CSV Open Jupyter or python Read file into a python dataframe Run latent dirichlet analysis code
- 58. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Json array to Pandas dataframe Reading Twitter json can be a pain. ● I wrote library clean_twitter.py to convert these to a CSV file ● Or you can use “df = pd.DataFrame([tweetdata]).transpose().reset_index()” (If you use Spacy, you might get a run error: fix (“python =m spacy download en” from the terminal window) is at https://stackoverflow.com/questions/54334304/spacy-cant-find-model-en-core-web-sm-on-windows-10- and-python-3-5-3-anacon ) 58
- 59. Disinformation/Malign Influence Training, Disarm Foundation | 2022 Topic detection using Latent Dirichlet Analysis no_features = 1000 no_topics = 7 tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words=stop_words) tfidf = tfidf_vectorizer.fit_transform(dftweets['text']) tfidf_feature_names = tfidf_vectorizer.get_feature_names() 59 lda = LatentDirichletAllocation(n_components=no_topics , max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tfidf) no_top_words = 10 for topic_idx, topic in enumerate(lda.components_): print("Topic %d:{}".format(topic_idx)) print(" ".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]])) from https://blog.mlreview.com/topic-modeling-with-scikit-learn-e80d33668730