Cognition & Biology & ICT  (CBI) Session 1

Abstract: Social media and mobile application data provide population-level observation tools with the potential to speed translational research. We describe recent work demonstrating Instagram’s importance for public surveillance of drug interactions [1]. Our methodology is based on the longitudinal analysis of Instagram user timelines at different timescales: day, week and month. Weighted graphs are built from the co-occurrence of terms from various biomedical dictionaries (drugs, symptoms, natural products, side-effects, sentiment) at the various timescales. We show that spectral methods, shortest-paths and distance closure analysis [2,3] reveals relevant drug-drug and drug-symptom pairs, as well as clusters of terms and drugs associated with complex pathology associated with depression [1]. We further extend the approach to three additional social media sources: Twitter, ChaCha and the Epilepsy Foundation public forums. We focus on drugs and symptoms of epilepsy, identifying patient cohorts at risk for Sudden, Unexpected Death in Epilepsy (SUDEP), the top epilepsy-related cause of death. Via the Epilepsy Foundation data, we collect social media timelines of patients who died from SUDEP. Training classifiers on a cohort with known medical outcome, allows us to test the potential of social media in the prediction of SUDEP as well as identifying the terminology and behavior associated with it. Since existing methods have failed to identify consistent etiologies for SUDEP, we show that social media mining may be helpful in identifying unknown factors and behavioral transitions that precede SUDEP, thus enhancing predictability. Finally, we discuss the generalization of the pathology to other conditions by showcasing a general-purpose web-tool we have been developing [1]. [1] R.B. Correia, L. Li, L.M. Rocha [2016]. Pac. Symp. Biocomp. 21:492-503. (PMCID: PMC4720984) [2] T. Simas and L.M. Rocha [2015]. Network Science, 3(2):227-268. [3] G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini [2015]. PLoS One. 10(6): e0128193. Close

Abstract: The carbon stored in the terrestrial biosphere, were it all released into the atmosphere instantaneously as carbon dioxide, would catastrophically change the Earth’s climate. Human actions that, both directly and indirectly, damage the integrity of the biosphere risk undermining’s the biosphere capacity to maintain this store of carbon. Here, we investigate the risk that degradation of the biosphere will trigger catastrophic climate change, even if future fossil emissions are kept to low levels. Whether terrestrial carbon stores can be maintained depends critically on the speed and strength of feedbacks involving the global carbon cycle, climate change, and dynamics of the biosphere. Many of the interactions that comprise these feedbacks are highly uncertain, such as the vulnerability of the biosphere to the magnitude and rate of temperature changes and how changes to the biosphere affect its ability to store carbon, and therefore are rarely implemented in climate models. We extend a previous stylised dynamical model of the global carbon cycle to include interactions with biosphere integrity. We use this model to integrate the range of current knowledge on climate-biosphere interactions and study its possible consequences. Our model constitutes a study of the interactions between the two core planetary boundaries: climate change and biosphere integrity. Close

Abstract: In many contexts, people do not speak their mind but falsify their private opinions (what Timur Kuran calls “preference falsification”). It goes from complimenting your boss for his ugly tie, not outing regarding one’s homosexual orientation, to publicly agreeing with public policies in a totalitarian country. Opinion falsification is still to be analyzed by means of precise multi-agent models. To this effect, we extend the bounded confidence (BC) model of opinion dynamics to analyze how much opinion falsification has an impact on well-entrenched results. In the initial BC model (Deffuant et al. 2002), agents update their opinions on random encounters with agents whose opinion differs from theirs by less than a common threshold d. One of the main results is the 1/2d rule: the number of final opinion clusters is the integer part of 1/2d. We keep the BC updating mechanism for private opinions but, in our model, the public opinion of agents is a compromise between their private opinion and what they previously heard from other agents. Overall, opinion falsification is characterized by two parameters: beta, which is the weight given to the opinion of others in the compromise mechanism, and memory size, the number of past interactions remembered by agents. We find that, even with tiny betas, important features of the BC model dynamics are altered. Total consensus obtains for thresholds d way lower than the one needed in the BC model, and all the more since beta (and opinion falsification) increases. The shift in the 1/2d curves is stronger when the model runs longer and these results are robust under parameters variations. Overall, there seems to be a two scale temporal dynamics: after converging to a meta-stable state following the 1/2d rule, all clusters end up merging into one. We quantitatively analyze the kinematics of these dynamics. Close

Abstract: People shape opinions about common topics based on their beliefs, prior knowledge, peer influence, and personal involvement or interest in a certain topic. Individuals, social groups, or companies get particularly active in proliferating their ideas if they find any benefit in it, either moral, personal, collective, spiritual or material. Online social networks provide a rich source of user generated content, and the direct or indirect interactions between the users. It has been shown that in such a complex system different groups of tightly connected users can share their opinions on some topics, but can also considerably differ in their preferences towards selected controversial topics [1]. In our work we study various aspects of the online debate on climate change and the associated environmental policies. We use Twitter data as the source of public opinion and construct a dynamic content sharing network between 7 Million users, created from over 40 Million tweets, acquired during the last two and half years. We apply various techniques for temporal network mining to track the evolution of different communities participating in the climate change debate, and to understand their content sharing patterns. Using text mining and sentiment analysis we detect the communities’ opinions and preferences on relevant issues and policies. We show that the climate change debate on Twitter is dominated, at the highest level of partitioning, by two contrarian groups of users. We observe distinctive discourse and use of vocabulary in the reactions to relevant news events or policy announcements. Furthermore, we confirm the engagement of climate change contrarians detected in [2] also in our data. [1] Sluban, B. et al.: Sentiment leaning of influential communities in social networks, Computational Social Networks, 2:9, 2015 [2] Farrell, J.: Network structure and influence of the climate change counter-movement, Nature Climate Change, 2015 Close