sas2016 - personality and the science of...
TRANSCRIPT
Personality and The Science of Sharing
Jason Baldridge Co-founder, People Pattern @jasonbaldridge
Preliminary notes• This talk incorporates results and images from many different research papers by people working primarily in social
network analysis. • As such, this talk is a synthesis of that work put together into a narrative to introduce key abilities and results. I felt this
high-level view was the best way to discuss “The Science of Sharing”, rather than relying primarily on my own work or work done at People Pattern. Also, I was really impressed by the work researchers are doing in social network analysis and wanted to share even a glimpse of the problems they are tackling and what they are finding.
• The high-level progression of this talk is: • Document analysis at scale: meme tracking combined with other variables like sentiment and bias • Social network at scale: information cascades and virality, inference of social networks given meme-like information as
contagions. • The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics,
personality and demographics. • Personas and segmentation: grouping based on demographics and interests.
• The last item is work done at People Pattern. I stress that neither I nor People Pattern was involved with the research papers cited in the other slides. My own academic research focuses on natural language processing, especially machine learning for learning syntactic parsers and performing geolocation using text. For more on those topics, see: http://www.jasonbaldridge.com/papers
• References and links to PDF’s of all cited work are at the end of this deck. They are also available on this post on my blog: http://bcomposes.com/2015/10/23/references-for-my-izeafest-talk/
Quoting Patterns in Political Coverage
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Measuring bias is subjective and hard. Personal estimates of bias are influenced by the availability heuristic.
57% of Americans perceive media as biased. 73% of conservatives think bias is liberal.
11% of liberals think bias is liberal.
Similarly: husbands and wives both estimate their contributions to family activities differently.
[Lee & Waite (2005): http://www.jstor.org/stable/3600272]
Read this!
Quoting Patterns in Political Coverage
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Automated tracking of quotations from Obama’s speeches.
Red: quoted in conservative media. Blue: quoted in
liberal media.
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Dimensionality reduction reveals two main bias dimensions: (one) independent-mainstream & (two) foreign-liberal-conservative.
Quoting Patterns in Political Coverage
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Sentiment across two bias dimensions: more mainstream & conservative correlates with negative sentiment.
Quoting Patterns in Political Coverage
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active. Information spreads from active nodes along the network edges.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Given information cascades, infer network using contagion model.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters. Red: mainstream media. Blue: blogs.
March 2011
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters. Red: mainstream media. Blue: blogs.
June 2011
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters. Red: mainstream media. Blue: blogs.
October 2011
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Evolution of network for Fukushima articles.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Evolution of network for Fukushima articles.
Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Blogs and mainstream media swap influence during course of event. Increased blog influence proportion correlates with social unrest.
Is virality/contagion a bad metaphor?
Taylor Swift has 65 million Twitter followers who can receive her messages. One individual cannot sneeze on
and infect that many people simultaneously.
The likelihood of disease infection increases independently with exposure to different infected
individuals, but “infection” by an idea increases greatly when exposed to it by multiple, independent parties.
Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others. A minority opinion can appear extremely popular for each individual (left side).
Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others. A minority opinion can appear extremely popular for each individual (left side).
Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others. A minority opinion can appear extremely popular for each individual (left side).
Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The size of majority illusion in Digg and political blogs, varying the number and connectedness of infected nodes.
Personality classification
Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers.”
Language production provides a window on personality at scale.
Personality classification
Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers.”
Language production provides a window on personality at scale.
Personality classification
Iacobelli et al. (2015). “Large Scale Personality Classification of Bloggers.”
Bigrams as indicators of high/low scorers in personality classification.
High scorers Low scorersNeuroticism
Extroversion
Openness
Agreeableness
Conscientiousness
Ad Targeting and Personality
Chen et al. (2015). “Making Use of Derived Personality: The Case of Social Media Ad Targeting.”
Twitter users whose language indicates higher openness and lower neuroticism are more likely to respond positively to an ad.
Dark Tetrad
Buckels et al 2014, “Trolls just want to have fun.” http://www.sciencedirect.com/science/article/pii/S0191886914000324
The favorite activity of people who score highly for the dark tetrad personality types is…. surprise… trolling!
Antisocial Behavior Online
Cheng et al. (2015). “Antisocial Behavior in Online Discussion Communities.”
Comparing banned & normal users (in retrospect): banned users wrote posts that are less relevant, harder to read, and less positive.
FBU: Future banned users NBU: Never banned users
Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Human analysis and machine learning can be used to characterize and identify personas using social media profiles.
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Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Interest prediction and extraction of interest-specific keywords. Promoted tweet copy informed by persona-based keywords.
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Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Persona-based campaigns with audience-driven ad copy produced higher engagement at lower cost per conversion.
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Conversions
0
60
120
180
240
Control Overscheduled Parent Grab & Go
Cost per conversion
0
10
20
30
40
Niche segmentation
Doresa Jennings Cheryl Baldridge
• PhD, BGSU • Lives in the southern USA • Mother of profoundly gifted
children • Homeschooler • Commitment to STEM • African-American
• JD, Yale • Lives in the southern USA • Mother of profoundly gifted
children • Homeschooler • Commitment to STEM • African-American
Dr. J creates a lot of original text and video. My busy wife makes time for it all.
Other content is less compelling for her.
http://kdacademy.blogspot.com/
https://www.youtube.com/user/DAJedu
Automated Segmentation
Unsupervised segmentation based on predicted interests to understand, expand and reach audiences.
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Interests
}cluster connections
Influencers
connections
connections
References• Chen et al. (2015). “Making Use of Derived Personality: The Case of Social Media Ad Targeting.”
- http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10508
• Cheng et al. (2015). “Antisocial Behavior in Online Discussion Communities.” - http://arxiv.org/abs/1504.00680
• Friggeri et al. (2015). “Rumor Cascades.” - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8122
• Goel et al. (2015). “The Structural Virality of Online Diffusion.” - https://5harad.com/papers/twiral.pdf
• Gomez-Rodriguez et al. (2014). “Quantifying Information Overload in Social Media and its Impact on Social Contagions.” - http://arxiv.org/abs/1403.6838
• Gomez Rodriguez et al. (2014). "Uncovering the structure and temporal dynamics of information propagation." - http://www.mpi-sws.org/~manuelgr/pubs/S2050124214000034a.pdf
• Iacobelli et al. (2015). “Large Scale Personality Classification of Bloggers.” - http://www.research.ed.ac.uk/portal/files/12949424/Iacobelli_Gill_et_al_2011_Large_scale_personality_classification_of_bloggers.pdf
References• Kang and Lerman (2015). “User Effort and Network Structure Mediate Access to Information in
Networks.” - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10483
• Kooti et al. (2015). “Evolution of Conversations in the Age of Email Overload.” - http://arxiv.org/abs/1504.00704
• Kulshrestha et al (2015). “Characterizing Information Diets of Social Media Users.” - https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10595/10505
• Lerman et al. (2015). “The Majority Illusion in Social Networks.” - http://arxiv.org/abs/1506.03022
• Leskovec et al. (2009). “Meme-tracking and the Dynamics of the News Cycle.” - http://www.memetracker.org/quotes-kdd09.pdf
• Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns.” - http://snap.stanford.edu/quotus/
• Weng et al. (2014). “Predicting Successful Memes using Network and Community Structure.” - http://arxiv.org/abs/1403.6199
• Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers.” - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2885844/