As part of our twitter bot experiment, my class on social graphs and interactions tried use our robots to start a #turkeyface trend on Twitter for thanksgiving. Looking at these photos, I simply don’t get why this meme didn’t take off :)
It’s not just the network structure that we care about. We want to understand network structure in order to get a handle on processes taking place on networks. That kind of processes is what next week’s two exciting (Monday and Tuesday October 6th and 7th, at 11am @ DTU) talks focus on. Both talks are open to the public, so I hope you’ll join us if you’re in Copenhagen. Full details here:
Cornelia Betsch on Vaccination Decision Making
- Time: Monday, October 6th, 2014
- Place: Technical University of Denmark, Building 321, 1st floor Lab Space
- Title: Vaccination decision making – an individual and social perspective
- Speaker: Dr. Cornelia Betsch. PD Dr. Cornelia Betsch is research fellow (Akademische Oberrätin) and scientific manager of the Center for Empirical Research in Economics and Behavioral Science (CEREB) at the University of Erfurt, Germany. She serves as a member of the European Technical Advisory Group of Experts on Immunization (ETAGE) of the WHO Europe and as a member of the German Commission for the Verification of Measles and Rubella Elimination(Federal Ministry of Health @ Robert Koch Institute).
- Abstract: The desperate search for a vaccine against Ebola currently reminds us on the merits and value of vaccination. Still, there is a small but critical amount of parents and adults who decide not to vaccinate their children or themselves. They endanger public health goals such as the elimination of diseases like measles or polio. In this talk I will show from the individual perspective what may influence a decision against vaccination. Further, I will analyze the vaccination decision from a structural point of view and show the social perspective of vaccination decision making: as many vaccinated individuals can protect some unvaccinated individuals, it may be rational to forego vaccination and to free ride. Given we know something about how people make vaccination decisions, which strategies should we choose for vaccine advocacy? In the final part of the talk I will give some examples and link them to real-world challenges of vaccine communication.
Jens Koed on Describing the psychology of argumentation
- Time: Tuesday, October 7th, 2014
- Place: Technical University of Denmark, Building 321, 1st floor Lab Space
- Title: Describing the psychology of argumentation, reasoning, and persuasion from a Bayesian perspective
- Speaker: Jens Koed Madsen (Postdoc @ Birkbeck, University of London)
- Abstract: Classical psychological models of persuasion and reasoning (Chaiken, 1980; Petty & Cacioppo, 1981) conceptualise rationality from the perspective of formal logical reasoning. Empirically, however, humans do not respond in line with logical predictions, as many fallacious arguments are accepted, and not all valid arguments are accepted. This has led to the conclusion that humans are not rational and to the development of the dual-process theory (consisting of a slow, laboured, and logical and a shallow, heuristic, and non-logical system). Recently, rationality has been recast as reasoning from uncertainty rather than reasoning from certainty from a Bayesian perspective (Oaksford & Chater, 2007). The paradigm has successfully been applied to reasoning (e.g. Oaksford & Chater, 2007), argumentation (e.g. Hahn & Oaksford, 2006; 2007), fallacies (e.g. Corner et al., 2011; Harris et al., 2012), persuasion (Madsen, 2013), and has integrated source credibility in a reasoning framework (Hahn et al., 2009; Harris et al., submitted). I work on three aspects of Bayesian persuasion: the conceptual development of the persuasion model from the thesis (Madsen, 2013), the psychological ontogenesis of probabilistic estimations, and the relationship between individualised approaches to belief changes and behaviour changes. These aspects touch upon the modelling, theoretical foundation, and application of the Bayesian approach developed in the past decade.
Bibliography for Jens’ talk
Chaiken, S. (1980) Heuristic versus systematic information processing and the use of source versus message cues in persuasion, Journal of Personality andSocial Psychology 39, 752-766
Corner, A., Hahn, U. & Oaksford, M. (2011). The psychological mechanism of the slippery slope argument. Journal of Memory & Language, 64, 133-152.
Hahn, U., Harris, A. J. L., & Corner, A. (2009). Argument content and argument source: An exploration. Informal Logic, 29, 337-367.
Hahn, U. & Oaksford, M. (2006a) A Bayesian Approach to Informal Reasoning Fallacies. Synthese 152, 207-23
Hahn, U., & Oaksford, M. (2007a) The rationality of informal argumentation: A Bayesian approach to reasoning fallacies, Psychological Review 114, 704-732
Hahn, U., Oaksford, M., & Harris, A. J. L. (2012). Testimony and argument: A Bayesian perspective. In F. Zenker (Ed.), Bayesian Argumentation (pp. 15-38). Dordrecht: Springer.
Harris, A. J. L., Hahn, U., Madsen, J. K. & Hsu, A. S. (submitted) The Appeal to Expert Opinion: Quantitative support for a Bayesian Network Approach, Cognitive Science, XXX, xxx-xxx
Madsen, J. K. (2013) Prolegomena to a Theory and Model of Persuasion Processing: A Subjective-Probabilistic Interactive Model of Persuasion (SPIMP), unpublished thesis, University College London
Oaksford, M. & Chater, N. (2007) Bayesian Rationality: The probabilistic approach to human reasoning. Oxford, UK: Oxford University Press.
Petty, R. E. & Cacioppo, J. T. (1981) Attitudes and persuasion: Classic and contemporary approaches, Boulder, CO: Westview Press
This friday we’re lucky to have visitor Emilio Ferrara presenting a talk on identifying twitter bots. Emilio’s work has been covered extensively in the media, for example MIT Technology Review’s How to spot a social bot on twitter. Details below:
- Date: Friday September 12th, 2014
- Time: 11:00-noon
- Place: DTU Building 321, first floor lab space
- Speaker: Emilio Ferrara (@), Post-doctoral Research Fellow at Indiana University Bloomington
- Title: The rise of social bots: fighting deception and misinformation on social media
- Abstract: One of the classic problems in Computer Science, recognizing the behavior of a human from that of a computer algorithm (proposed by Alan Turing), has suddenly become very relevant in the context of social media. Limits to the expressive power of humans and real incentives abound to develop human-mimicking software agents called social bots. These elusive entities wildly populate social media ecosystems, often going unnoticed among the population of real people. Bots can be harmful, aiming at persuading, smearing, or deceiving, and for such a reason our research aims at developing efficient systems to detect them. In my talk I will discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. Characteristics related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering. I will present “Bot or Not?”, a social bot detection framework prototype developed at Indiana University under the Truthy project. My talk will conclude depicting future scenarios and discussing related problems, such as that of studying persuasion campaigns on social media, how they spread, and how we can promptly detect and potentially hinder their diffusion.
Network science buffs are in for a treat this Monday (September 1st, 2014), when we have a great set of visitors in my Group at DTU. I’m excited to present talks on the cutting edge on what we know about networks from János Kertész and Janos Török. The talks will be back to back and detailed info can be found below
The talks are open to the public, so hope to see you there!
- Time: Monday September 1st, 10am – noon
- Place: DTU Building 321, room 134 (1st floor lab area).
Multi-level, multi-channel, multi-agent modeling of social interactions (János Török)
Abstract: We present a model of society. Human relations are strengthened by communication and eroded by time. Communication is, in general, related to some social activity (work, friendship, hobby) or social context. Therefore we postulate that individuals having different social needs participate in a number of social contexts (family, workplace etc.) – which may also evolve in time – and communicate with other members of the contexts using different communication channels (face to face, phone, email, etc.) for different purposes and with different impact on their relationship. We show that using realistic input data from surveys and statistical data one can reproduce important features of real society like Dunbar’s numbers and their meaning.
Spreading on temporal networks: Results from empirical analysis, model calculations and simulation (János Kertész)
Abstract: Spreading phenomena typically take place on temporal networks, where connections between the nodes are only occasionally and for limited time present. Such events can be, e.g., encounters of people, which are important for contagion or opening a communication channel needed for information transmission. We studied a mobile call network from this point of view: Having the time stamped records of the calls we played a ‘susceptible-infected’ game by infecting one node at random and assuming transmission at every possible event. We introduced different reference systems by appropriate shuffling of the data and identified this way the contributions of the different types of correlations to the speed of spreading. We concluded that there is a considerable slowing down as compared to the random models, mainly due to the correlations between the link weights and the topology and the inhomogeneous, bursty character of the events. We have also shown that the temporal inhomogeneity cannot be characterized by the inter-event time distribution (IETD) alone as there are strong dependencies between the events. In order to understand better the role of the different components we investigated models of temporal networks. In the analytically solvable infinite complete graph we showed that burstiness, i.e., power law IETD distribution always accelerates the process provided the clocks are positioned on the nodes. For the complementary case of link related burstiness we considered a number of models, like the analytically tractable Cayley tree, BA trees and networks. We show that if the stationary bursty process is governed by power-law IETD, the spreading can be slowed down or accelerated as compared to a Poisson process; the speed is determined by the short time behavior, which in our model is controlled by the exponent. We demonstrate that finite, so called “locally tree-like” networks, like the Barabási-Albert networks behave very differently from real tree graphs if the IETD is strongly fat-tailed, as the lack or presence of rare alternative paths modifies the spreading. A further important result is that the non-stationarity of the dynamics has a significant effect on the spreading speed for strongly fat-tailed power-law IETDs, thus bursty processes characterized by small power-law exponents can cause slow spreading in the stationary state but also very rapid spreading heavily depending on the age of the processes.
1. M. Karsai, M. Kivelä, R. K. Pan, K. Kaski, J. Kertész, A.-L. Barabási, J. Saramäki: Small But Slow World: How Network Topology and Burstiness Slow Down Spreading, Phys. Rev. E 83, 025102 (2011)
2. Márton Karsai, Kimmo Kaski, Albert-László Barabási, János Kertész: Universal features of correlated bursty behavior, Scientific Reports 2, Article number 397 (2012)
3. Márton Karsai, Kimmo Kaski, János Kertész: Correlated dynamics in egocentric communication networks, PLoS ONE 7(7) e40612 (2012)
4. Hang-Hyun Jo, Márton Karsai, János Kertész, Kimmo Kaski: Circadian pattern and burstiness in human communication activity, New J. Phys. 14 013055 (2012)
5. Szabolcs Vajna, Bálint Tóth, János Kertész: Modelling power-law distributed interevent times, New J. Phys.15, article 103023 (2013)
6. Hang-Hyun Jo, Juan I. Perotti, Kimmo Kaski, János Kertész: Enhanced Spreading Dynamics by Non-Poissonian Processes, Physical Review X 4, 011041 (2014)
7. Dávid X. Horváth, János Kertész: Spreading dynamics on networks: the role of burstiness, topology and non-stationarity, New Journal of Physics 16 (7), 073037
As part of a Master’s project, Marta Magiera, a student in my group has developed a great tool for visualizing geo-data. Check it out below (looks best in 720p)
I often give the following writing advice to my students. Today, in honor of efficiency, I decided I’d put my advice in a blog post, so I can just link to it in the future.
Unless you’re a great writer (in which case you don’t have to follow any rules), the structure of academic text is the following:
- First you tell your readers what you’re about to tell them.
- Then you tell the readers the thing you want to tell them.
- Finally you tell them what you’ve just told them.
This structure works on a number of levels in a thesis.
On the level of the entire thesis, the introduction tells the reader what’s going to happen in the text and the conclusion summarizes what just happened, while the chapters in between contain the actual work.
But for each chapter, you should also put an introduction and conclusion around the content, and similarly for each section. Even within each subsection, it might be good idea to start with a introductory sentence or two (setting the stage) and wrapping up. You have to stop before it gets too pedantic, but I hope the point gets across. It’s not exactly fractal, but almost.
With collaborators at MIT (first author is Yves-Alexandre de Montjoye) we have just published a paper in Scientific Reports, The Strength of the Strongest Ties in Collaborative Problem Solving.
The paper shows that networking (in the sense of building a larger network of weak ties) does not improve team performance under some circumstances. We showed that for teams of knowledge workers in a competitive environment, the strongest ties (best friends or people you spend a lot of time with) explain much of the team performance in our statistical model.
Said differently, a team’s strongest ties are the best predictor of how the team will perform. They predict performance better than any other factors we looked at such as the technical abilities of its members, how knowledgeable they are about the topic at hand, and even their personality. In fact, once you account for a team’s strongest ties none of these other factors matters.
A neat infographic (created by Yves) explains the main findings and shows some of the key plots.