phil 7.12.18

Stampede thinking:

  • Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning
    • Gordon Pennycook
    • David Rand
    • Why do people believe blatantly inaccurate news headlines (“fake news”)? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology? Here we test these competing accounts in two studies (total N = 3446 Mechanical Turk workers) by using the Cognitive Reflection Test (CRT) as a measure of the propensity to engage in analytical reasoning. We find that CRT performance is negatively correlated with the perceived accuracy of fake news, and positively correlated with the ability to discern fake news from real news – even for headlines that align with individuals’ political ideology. Moreover, overall discernment was actually better for ideologically aligned headlines than for misaligned headlines. Finally, a headline-level analysis finds that CRT is negatively correlated with perceived accuracy of relatively implausible (primarily fake) headlines, and positively correlated with perceived accuracy of relatively plausible (primarily real) headlines. In contrast, the correlation between CRT and perceived accuracy is unrelated to how closely the headline aligns with the participant’s ideology. Thus, we conclude that analytic thinking is used to assess the plausibility of headlines, regardless of whether the stories are consistent or inconsistent with one’s political ideology. Our findings therefore suggest that susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se – a finding that opens potential avenues for fighting fake news.

From Alessandro Bozzon (Scholar):

  • I am Assistant Professor with the Web Information Systemsgroup, at the Delft University of Technology. I am Research Fellow at the AMS Amsterdam Institute for Advanced Metropolitan Solutions, and a Faculty Fellow with the IBM Benelux Center of Advanced Studies.

    My research lies at the intersection of crowdsourcing, user modeling, and web information retrieval. I study and build novel Social Data science methods and tools that combine the cognitive and reasoning abilities of individuals and crowds, with the computational powers of machines, and the value of big amounts of heterogeneous data.

    I am currently active in three investigation lines related to Social Data Science: Intelligent Cities (SocialGlass; Crowdsourced Knowledge Creation in Online Social Communities (SEALINCMedia COMMIT/StackOverflow); and Enterprise Crowdsourcing (with IBM Benelux CAS).

  • Modeling CrowdSourcing Scenarios in Socially-Enabled Human Computation Applications
    • User models have been defined since the 1980s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing/human computation platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of social users and performers, including their social ties, interests, capabilities, activity history, and topical affinities. In this paper, we define a comprehensive model able to cater for all the aspects relevant for applications involving social networks and human computation; we capitalize on existing social user models and content description models, enhancing them with novel models for human computation and gaming activities representation. Finally, we report on our experiences in adopting the proposed model in the design and implementation of three socially enabled human computation platforms.
  • Sparrows and Owls: Characterisation of Expert Behaviour in StackOverflow
    • Question Answering platforms are becoming an important repository of crowd-generated knowledge. In these systems a relatively small subset of users is responsible for the majority of the contributions, and ultimately, for the success of the Q/A system itself. However, due to built-in incentivization mechanisms, standard expert identification methods often misclassify very active users for knowledgable ones, and misjudge activeness for expertise. This paper contributes a novel metric for expert identification, which provides a better characterisation of users’ expertise by focusing on the quality of their contributions. We identify two classes of relevant users, namely sparrows and owls, and we describe several behavioural properties in the context of the StackOverflow Q/A system. Our results contribute new insights to the study of expert behaviour in Q/A platforms, that are relevant to a variety of contexts and applications.

Phil 7.1.18

On vacation, but oddly enough, I’m back on my morning schedule, so here I am in Bormio, Italy at 4:30 am.

I forgot my HDMI adaptor for the laptop. Need to order one and have it delivered to Zurich – Hmmm. Can’t seem to get it delivered from Amazon to a hotel. Will have to buy in Zurich

Need to add Gamerfate to the lit review timeline to show where I started to get interested in the problem – tried it but didn’t like it. I’d have to redo the timeline and I’m not sure I have the excel file

Add vacation pictures to slides – done!

Some random thoughts

  • When using the belief space example of the table, note that if we sum up all the discussions about tables, we would be able to build a pretty god map of what matters to people with regards to tables
  • Manifold learning is what intelligent systems do as a way of determining relationships between things (see curse of dimensionality). As groups of individuals, we need to coordinate our manifold learning activities so that we can us the power of group cognition. When looking at how manifold learning schemes like t-sne and particularly embedding systems such as word2vec create their own unique embeddings, it becomes clear that our machines are not yet engaged in group cognition, except in the simplest way of re-using trained networks and copied hyperparameters. This is very prone to stampedes
  • In conversation at dinner, Mike M mentioned that he’d like a language app that is able to indicate the centrality of a term an order that list so that it’s possible to learn a language in a “prioritized” way that can be context-dependent. I think that LMN with a few tweaks could do that.

Continuing the Evolution of Cooperation. A thing that strikes me is that once a TIT FOR TAT successfully takes over, then it becomes computationally easier to ALWAYS COOPERATE. That could evolve to become dominant and be completely vulnerable to ALWAYS DEFECT

Phil 6.27.18

7:00 – 12:00 ASRC MKT

  • Print out documents! Done. Got passport drive too.
  • Need to write an extractor that lets the user navigate the xml file containing influences of selected agents. This could be a sample-by sample network. Maybe two modes?
    • Select an agent and see all the other agents come in and out of influcene
    • Select an number of agents and only watch the mutual influence.
    • There is an integrated JavaFX charts that I could use, or it could be an uploaded webapp? JavaFX would be easier in the short term, but a webapp would help more with JuryRoom…
    • Another option would be Python, since that’s where the LSTM code will live.
    • On the whole, two days before leaving on travel is probably the wrong time to start coding
  • Fixed a bug in the xml file generation
  • copied the new jar file onto the thumb drive
  • copied the xml file onto the thumb drive

12:00 – 4:00 ASRC A2P

  • Pomoting things to QA – done! Or at least, up to date with the excel files

Phil 6.26.18

7:00 – 5:00 ASRC MKT

  • Started back with the Evolution of Cooperation
  • Social loafing (Scholar results)
    • In social psychologysocial loafing is the phenomenon of a person exerting less effort to achieve a goal when they work in a group than when they work alone. This is seen as one of the main reasons groups are sometimes less productive than the combined performance of their members working as individuals, but should be distinguished from the accidental coordination problems that groups sometimes experience. Research on social loafing began with rope pulling experiments by Ringelmann, who found that members of a group tended to exert less effort in pulling a rope than did individuals alone. In more recent research, studies involving modern technology, such as online and distributed groups, have also shown clear evidence of social loafing. Many of the causes of social loafing stem from an individual feeling that his or her effort will not matter to the group.
  • NELA2017 contains almost every news article from 92 sources between April 2017 and October 2017, amounting to over 136K articles. This data set is the first release of NELA datasets. This version of the data set can be found on github and a full description and use cases can be found in our 2018 ICWSM paper.
  • Submitted “One Simple Trick” final to SASO
  • Updated ArXive
  • Fixed a bug that prevented population interactions in FlockingAgentManager.initializeAgents():
                // add to the global list
                allBoidsList.add(fs);
    
                // add a pointer to the global list to each shape
                fs.setFlockingShapeList(allBoidsList);
    
                // Add to the flock so that we can get flock headings
                List flock = flockListsMap.get(flockName);
                flock.add(fs);

    Seriously, what was I thinking?

  • Continued GUI tweaking. I think it looks pretty good, and it fits (mostly) on my laptop Version6.26.18
  • Verified that the influences record agents from different flocks and sources.
  • Copied all CI 2018 things I can think of onto the thumb drive

Phil 6.23.18

Registered for SASO

ArXive papers with Github repos

Mapping interest communities in Russian Facebook Ads. Preliminary visualisation reveals a number of broad interest groups around ethnicity; reveals a bit of Internet Research Agency’s strategy...

  • Dr Bharath Ganesh
    • Bharath is a political geographer focusing on data science and local government and the ethics and politics of researching violent online extremism.

More good stuff from Ian Couzin

  • Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion
    • We know little about the nature of the evolved interaction networks that give rise to the rapid coordinated collective response exhibited by many group-living organisms. Here, we study collective evasion in schooling fish using computational techniques to reconstruct the scene from the perspective of the organisms themselves. This method allows us to establish how the complex social scene is translated into behavioral response at the level of individuals and to visualize, and analyze, the resulting complex communication network as behavioral change spreads rapidly through groups. Thus, we can map, for any moment in time, the extent to which each individual is socially influential during collective evasion and predict the magnitude of such behavioral epidemics before they actually occur

This playlist contains tutorials to learn how to use Keras, a neural network API written in Python. Each video focuses on a specific concept and shows how the full implementation is done in code using Keras and Python.

 

Phil 6.22.18

7:00 – 5:30 ASRC MKT

  • Twitter experiment on a fake Gary Indiana secession. IFTTT retweeting leads to interesting behavior.
  • Fixed FlockingShape casting by adding a customDrawStep(GraphicsContext gc) to the SmartShape base class that’s called from draw().
  • Add records to each agent that store a list of source and agent influences at each time sample. It should include the name of the item and the amount of influence. Probably save as an XML file, since it has too many dimensions. The file could then be used to create terms or spreadsheets.
    • Started on CAInfluence class which will be added to CA classes in an arrayList in BaseCA;
  • More file conversion with Bob – and everything worked great until I try one after Bob leaves. Ka-BOOM!
    • Installed all the packages to get everything to run in the debugger. Found what appears to be a perfectly good line “range” that causes the problem? Will start debugging on Wednesday.
  • Project MERCATOR proposal
  • Meeting with Sy

Phil 6.21.18

7:00 – 4:00 ASRC MKT

  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add attraction radius slider that is independent of the SIH. -done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • There is now an opportunity for a poster and a demo at SASO
  • Add stories, lists and maps to implication slides – done
  • Got all my connections set up
  • Successfully converted and deployed cosmos-2
  • Voted!

Phil 6.20.18

7:00 – 9:00 2:00 – 5:00 ASRC MKT

  • Redo doodle for all of August – done
  • Schooling Fish May Offer Insights Into Networked Neurons
    • Iain Couzin is deciphering the rules that govern group behavior. The results might provide a fresh perspective on how networks of neurons work together.
  • City arts and lectures: The New Science Of Psychedelics With Michael Pollan
    • Psychedelics reduce the section of the brain that have to do with the sense of self. Pollan thinks that this also happens with certain types of rhythmic music and in crowd situations. This could be related to stampedes and flocking.
    • LSD May Chip Away at the Brain’s “Sense of Self” Network
      • Brain imaging suggests LSD’s consciousness-altering traits may work by hindering some brain networks and boosting overall connectivity
  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done?
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • Worked on A2P white paper with Aaron.
  • Worked on a response to Dr. Li’s response

ASRC IRAD 9:00 – 2:00

  • Mind meld with Bob
    • Revisit Yarn
    • Excel stuff?
    • Connect to AWS using bastion. Look in FoxyProxy how to. I need certs
    • Drop on rabbit to deploy to CI and QA and NESDIS  ONE (production)
    • Don’t want sensitive information in Git. We use sharepoint instead
    • Notes and screenshots in document.

Phil 6.19.18

7:00 – 9:00, 4:00 – 5:00 ASRC MKT

  • Here’s a list of organizations that are mobilizing to help immigrant children separated from their families
  • SASO trip
  • Rebuilt all the binaries, now I need to put them on the thumb drive – done
  • Added knobs to the implications slide. They sit next to the dimension and SIH lines. I realize that my slide deck is becoming a physical version of a memory palace.
  • Continuing Irrational Exuberance, though feeling like I should be reading Axelrod. Bring Evolution of Cooperation on the flight?
  • Naive Diversification Strategies in Defined Contribution Saving Plans
    • There is a worldwide trend toward defined contribution saving plans and growing interest in privatized social security plans. In both environments, individuals are given some responsibility to make their own asset allocation decisions, raising concerns about how well they do at this task. This paper investigates one aspect of the task, namely diversification. We show that many investors have very naive notions about diversification. For example, some investors follow what we call the 1/n strategy: they divide their contributions evenly across the funds offered in the plan. When this strategy (or others only slightly more sophisticated) is used, the assets chosen depend greatly on the make-up of the funds offered in the plan. We find evidence of naive diversification strategies both in experiments using employees at the University of California and the actual behavior of participants in a wide range of savings plans. In particular, we find the proportion of the assets the participants invest in stocks depends strongly on the proportion of stock funds in the plan. The results raise very serious questions about how privatized social security systems should be designed, questions that would be ignored in most economic analyses.
    • This is very much a dimension reduction exercise.
  • A2P maintenance proposal

9:00 – 4:00 ASRC A2P

  • Coming up to speed on the Angular interface
    • Logging into CI and QA
    • Dashboard configurations

Phil 6.18.18

ASRC MKT 7:00 – 8:00

  • Nice ride on Saturday on Skyline drive
  • Using Social Network Information in Bayesian Truth Discovery
    • We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents’ reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An agent can belong to different communities for different events, and these communities are unknown a priori. We incorporate knowledge of the agents’ social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents’ reliabilities, communities, and the event states. We also develop a stochastic variational inference method to scale our model to large social networks. Simulations and experiments on real data suggest that when observations are sparse, our proposed methods perform better than several other inference methods, including majority voting, the popular Bayesian Classifier Combination (BCC) method, and the Community BCC method.
  • Scale-free correlations in starling flocks
    • From bird flocks to fish schools, animal groups often seem to react to environmental perturbations as if of one mind. Most studies in collective animal behavior have aimed to understand how a globally ordered state may emerge from simple behavioral rules. Less effort has been devoted to understanding the origin of collective response, namely the way the group as a whole reacts to its environment. Yet, in the presence of strong predatory pressure on the group, collective response may yield a significant adaptive advantage. Here we suggest that collective response in animal groups may be achieved through scale-free behavioral correlations. By reconstructing the 3D position and velocity of individual birds in large flocks of starlings, we measured to what extent the velocity fluctuations of different birds are correlated to each other. We found that the range of such spatial correlation does not have a constant value, but it scales with the linear size of the flock. This result indicates that behavioral correlations are scale free: The change in the behavioral state of one animal affects and is affected by that of all other animals in the group, no matter how large the group is. Scale-free correlations provide each animal with an effective perception range much larger than the direct inter-individual interaction range, thus enhancing global response to perturbations. Our results suggest that flocks behave as critical systems, poised to respond maximally to environmental perturbations.
  • Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study
    • By reconstructing the three-dimensional positions of individual birds in airborne flocks of a few thousand members, we show that the interaction does not depend on the metric distance, as most current models and theories assume, but rather on the topological distance. In fact, we discovered that each bird interacts on average with a fixed number of neighbors (six to seven), rather than with all neighbors within a fixed metric distance. We argue that a topological interaction is indispensable to maintain a flock’s cohesion against the large density changes caused by external perturbations, typically predation. …
  • Thread on the failure to replicate the Stanford Prison Experiment by Alex Haslam (scholar) (home page). Paper coming soon
    • The Stanford Prison Experience—as it is presented in textbooks—presents human nature as naturally conforming to oppressive systems. This is a lesson that extends well beyond prison systems and the field criminology—but it’s wrong. Alex and his colleagues (especially Steve Reicher) have been arguing for years that conformity often emerges when leaders cultivate a sense of shared identity. This is an active, engaged process—very different from automatic and mindless conformity.
  • Started Irrational Exuberance, by Robert Shiller
  • Send note to Don, Aaron and Shimei
  • Read Ego-motion in Self-Aware Deep Learning on Medium. It’s about reflective learning of navigation in physical spaces, though I wonder if there is an equivalent process in belief spaces. Looked through scholar and
  • Slide prep and Fika walkthrough
    • Went well. Ravi suggested adding another slide that discusses the methods in detail, while Sy pretty much demanded that I get rid of “Questions” and put the title of the paper in its place
    • When adding the detail for Ravi, I discovered that the simulator and map reconstruction did not handle single, high dimensional agents well, so I spent a few hours fixing bugs to get the screen captures to build the slides.

Phil 6.13.18

7:00 – 4:00 ASRC MKT

  • International driver’s license – done
  • Add visually-impaired labels to paper – done
  • Start slides
  • Interesting article on dimension reduction: The faces of God in America: Revealing religious diversity across people and politics What strikes me about this study is actually how similar the depictions are. In belief space, this would be a closely woven neighborhood. It would be interesting to see an equivalent study on a less anthropomorphic deity like Vishnu… journal.pone.0198745.g002
    • Literature and art have long depicted God as a stern and elderly white man, but do people actually see Him this way? We use reverse correlation to understand how a representative sample of American Christians visualize the face of God, which we argue is indicative of how believers think about God’s mind. In contrast to historical depictions, Americans generally see God as young, Caucasian, and loving, but perceptions vary by believers’ political ideology and physical appearance. Liberals see God as relatively more feminine, more African American, and more loving than conservatives, who see God as older, more intelligent, and more powerful. All participants see God as similar to themselves on attractiveness, age, and, to a lesser extent, race. These differences are consistent with past research showing that people’s views of God are shaped by their group-based motivations and cognitive biases. Our results also speak to the broad scope of religious differences: even people of the same nationality and the same faith appear to think differently about God’s appearance.
  • Finished paper
  • Working on talk

personal

  • Shopping – done
  • taxes
  • laundry – done
  • generator/un-grounded short extension cord – done. Works!

Phil 6.12.18

7:00 – 4:30 ASRC MKT

  • Listening to Clint Watts on his new book
    • “When you don’t know what to believe, you will fall back on your biases”
    • 3 levels of Russian recruitment
      • Useful Idiot
      • Fellow Traveler
      • Agent
    • “They don’t have to make up fake news, There is plenty of fake news for them to employ”
    • Huh. He’s responsible for Hamilton 68, and is interested to extending to beyond Russian Misinfo.
  • Polarization and Fake News: Early Warning of Potential Misinformation Targets
    • Walter Quattrociocchi (scholar)
    • Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.
  • Trend of Narratives in the Age of Misinformation
    • Walter Quattrociocchi (scholar)
    • Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a {\em disintermediation} weakened consensus on social relevant issues in favor of rumors, mistrust, and fomented conspiracy thinking — e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. 
      In this work, we study through a thorough quantitative analysis how different conspiracy topics are consumed in the Italian Facebook. By means of a semi-automatic topic extraction strategy, we show that the most discussed contents semantically refer to four specific categories: environment, diet, health, and {\em geopolitics}. We find similar patterns by comparing users activity (likes and comments) on posts belonging to different semantic categories. However, if we focus on the lifetime — i.e., the distance in time between the first and the last comment for each user — we notice a remarkable difference within narratives — e.g., users polarized on geopolitics are more persistent in commenting, whereas the less persistent are those focused on diet related topics. Finally, we model users mobility across various topics finding that the more a user is active, the more he is likely to join all topics. Once inside a conspiracy narrative users tend to embrace the overall corpus.
  • More SASO paper
    • Finished explanation of the one simple trick
    • Need to add accessibility descriptions for pix

Phil 6.8.18

7:00 – 3:30 ASRC MKT

  • We should attend this:  IEEE International Symposium on Technology and Society
    • Nov. 13 & 14th, Washington DC
    • ISTAS is a multi-disciplinary and interdisciplinary forum for engineers, policy makers, entrepreneurs, philosophers, researchers, social scientists, technologists, and polymaths to collaborate, exchange experiences, and discuss the social implications of technology.
  • More Bit by Bit
    • This looks really good. It’s on how social networks and behavior co-evolve: Social selection and peer influence in an online social network
      • Disentangling the effects of selection and influence is one of social science’s greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes.
  • Cleaning up the SASO paper. Lots of good suggestions.
  • Got Aaron up to 16.5 on the 16 mile loop today!

Phil 6.7.18

7:00 – 4:30 ASRC MKT

  • Che Dorval
  • Done with the whitepaper! Submitted! Yay! Add to ADP
  • The SLT meeting went well, apparently. Need to determine next steps
  • Back to Bit by Bit. Reading about mass collaboration. eBird looks very interesting. All kinds of social systems involved here.
    • Research
      • Deep Multi-Species Embedding
        • Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project \textit{eBird}, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.
  • Start fixing This one Simple Trick
    • Highlighted all the specified changes. There are a lot of them!
    • Started working on figure 2, and realized (after about an hour of Illustrator work) that the figure is correct. I need to verify each comment before fixing it!
  • Researched NN anomaly detection. That work seems to have had its heyday in the ’90s, with more conventional (but computationally intensive) methods being preferred these days.
  • I also thought that Dr. Li’s model had a time-orthogonal component for prediction, but I don’t think that’s true. THe NN is finding the frequency and bounds on its own.
  • Wrote up a paragraph expressing my concerns and sent to Aaron.