Data Science Popup Austin: The Science of Sharing
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The document discusses a presentation by Jason Baldridge on the 'science of sharing' at the Data Science Pop Up in Austin. It synthesizes research in social network analysis, covering topics like meme tracking, information cascades, personal demographics, and personality classification. The conclusion emphasizes the importance of authentic content and understanding audience demographics for effective communication in a saturated information environment.
![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!](https://image.slidesharecdn.com/thescienceofsharingdatasciencepopupaustin-160421220435/85/Data-Science-Popup-Austin-The-Science-of-Sharing-8-320.jpg)
Data Science Popup Austin: The Science of Sharing
- 1. DATA SCIENCE POP UP AUSTIN The Science of Sharing Jason Baldridge Co-Founder / Chief Scientist, People Pattern jasonbaldridge
- 2. DATA SCIENCE POP UP AUSTIN #datapopupaustin April 13, 2016 Galvanize, Austin Campus
- 4. The Science of Sharing Jason Baldridge Co-founder, People Pattern Associate Professor, The University of Texas at Austin @jasonbaldridge
- 5. 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: https://bcomposes.wordpress.com/2015/10/23/references-for-my-izeafest-talk/
- 6. Meme tracking Leskovec et al. (2009). “Meme-tracking and the Dynamics of the News Cycle.” Automatic detection and tracking of memes over time.
- 7. Meme tracking Leskovec et al. (2009). “Meme-tracking and the Dynamics of the News Cycle.” Meme oscillation heartbeat from blogs to mainstream media.
- 8. 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!
- 9. 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.
- 10. 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
- 11. 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
- 12. Structural Virality Goel et al. (2015). “The Structural Virality of Online Diffusion” Information cascades can propagate via broadcast and viral diffusion. Most cascades contain both broadcast and viral spreading. Broadcast Viral
- 13. Structural Virality Goel et al. (2015). “The Structural Virality of Online Diffusion” Twitter cascades characterized by structural virality, increasing down and to the right.
- 14. Structural Virality Goel et al. (2015). “The Structural Virality of Online Diffusion” Petition cascades are smallest, but have highest structural virality.
- 15. Structural Virality Goel et al. (2015). “The Structural Virality of Online Diffusion” 99% of content adoptions terminate in a single generation The largest image and video cascades are low on structural virality. Broadcast is by far the dominant mode to reach large audiences. This means pay-to-play when you need to go big reliably.
- 16. 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.
- 17. Information propagation Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.” Given information cascades, infer network using contagion model.
- 18. 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 June 2011 October 2011
- 19. Information propagation Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.” Evolution of network for Fukushima articles.
- 20. Information propagation Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.” Information generally flows from mainstream media to blogs. Blogs play a crucial role in information dissemination in civil movements.
- 21. 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.
- 22. 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.
- 23. Majority illusion Lerman et al. (2015). “The Majority Illusion in Social Networks.” Friendship paradox: on average most people have fewer friends than their friends. This generalizes to any node attribute, which may explain why people overestimate their friends’ alcohol consumption.
- 24. 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).
- 25. 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.
- 26. 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.
- 27. 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 scorers Neuroticism Extroversion Openness Agreeableness Conscientiousness
- 28. 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.
- 29. 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
- 30. Race and sharing http://www.theatlantic.com/technology/archive/2015/10/race-social-media/408889/ Frequency of sharing for topics on social media varies by race. Events or entertainment Education or schools Re “race”: please read this book.
- 31. 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. +
- 32. 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. +
- 33. 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. + Conversions 0 60 120 180 240 Control Overscheduled Parent Grab & Go Cost per conversion 0 10 20 30 40
- 34. Sub-micro segmentation We have limited attention and many options. The best, most relevant content is often created by those with very similar passions, interests, and demographics. 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
- 35. Conclusion Authentic, original content is the most compelling. Audience understanding is essential: demographics, personality and microsegment relevance. Pay-to-play to reliably get your word out. Content consumers must constantly manage information overload. Large scale analysis of networks and documents reveals hidden patterns.
- 36. 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
- 37. 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/