How epidemic psychology works on Twitter: evolution of responses to the COVID-19 pandemic in the U.S.

Disruptions resulting from an epidemic might often appear to amount to chaos but, in reality, can be understood in a systematic way through the lens of "epidemic psychology". According to Philip Strong, the founder of the sociological study of epidemic infectious diseases, not only is an epidemic biological; there is also the potential for three psycho-social epidemics: of fear, moralization, and action. This work empirically tests Strong's model at scale by studying the use of language of 122M tweets related to the COVID-19 pandemic posted in the U.S. during the whole year of 2020. On Twitter, we identified three distinct phases. Each of them is characterized by different regimes of the three psycho-social epidemics. In the refusal phase, users refused to accept reality despite the increasing number of deaths in other countries. In the anger phase (started after the announcement of the first death in the country), users' fear translated into anger about the looming feeling that things were about to change. Finally, in the acceptance phase, which began after the authorities imposed physical-distancing measures, users settled into a "new normal" for their daily activities. Overall, refusal of accepting reality gradually died off as the year went on, while acceptance increasingly took hold. During 2020, as cases surged in waves, so did anger, re-emerging cyclically at each wave. Our real-time operationalization of Strong's model is designed in a way that makes it possible to embed epidemic psychology into real-time models (e.g., epidemiological and mobility models).Comment: Humanities and Social Sciences Communications. 24 pages, 7 figures, 4 table

Heart Rate Extraction from Abdominal Audio Signals

Abdominal sounds (ABS) have been traditionally used for assessing gastrointestinal (GI) disorders. However, the assessment requires a trained medical professional to perform multiple abdominal auscultation sessions, which is resource-intense and may fail to provide an accurate picture of patients' continuous GI wellbeing. This has generated a technological interest in developing wearables for continuous capture of ABS, which enables a fuller picture of patient's GI status to be obtained at reduced cost. This paper seeks to evaluate the feasibility of extracting heart rate (HR) from such ABS monitoring devices. The collection of HR directly from these devices would enable gathering vital signs alongside GI data without the need for additional wearable devices, providing further cost benefits and improving general usability. We utilised a dataset containing 104 hours of ABS audio, collected from the abdomen using an e-stethoscope, and electrocardiogram as ground truth. Our evaluation shows for the first time that we can successfully extract HR from audio collected from a wearable on the abdomen. As heart sounds collected from the abdomen suffer from significant noise from GI and respiratory tracts, we leverage wavelet denoising for improved heart beat detection. The mean absolute error of the algorithm for average HR is 3.4 BPM with mean directional error of -1.2 BPM over the whole dataset. A comparison to photoplethysmography-based wearable HR sensors shows that our approach exhibits comparable accuracy to consumer wrist-worn wearables for average and instantaneous heart rate.Comment: ICASSP 202

Status Report and Beam Time Request for Experiment AD-4

Summary of current status and plans for October 200

Data science for sociotechnical systems - from computational sociolinguistics to the smart grid

We live in the Information Age characterized by the exponential growth of the technological capacity to produce and store data (big data) and to process them towards information and knowledge (data science). In particular, large amounts of data are produced during the interaction between people and technology in diverse sociotechnical systems. Data science, as a set of theories and techniques to distill knowledge from data, is recognized as an effective tool to support sociotechnical systems. This dissertation consists of four projects, in which we apply data science for monitoring and interventions in concrete sociotechnical systems: human dynamics, social networks, smart grid and Web cybersecurity. By analyzing mobile phone communication from a developing country, we show how people's socio-economic factors correlate with their dynamics inferred from the data. Consequently, we demonstrate how monitoring mobile phone network can serve as a proxy for census statistics. In developing countries, where censuses are rare and infrequent, this can prove important. Using the Twitter data, we investigate two social phenomena: homophily and the happiness paradox. In addition to finding the evidence for respective sociological theories, we also provide interesting hypotheses for further investigation. In another, theoretical study, we propose an epidemic spreading model for multiplex networks (representing, for instance, user engagement is several social networks). The simulations reveal when the spreading dynamics of the whole system is slower compared to any individual layer. Our model can be employed by the governments, companies, and others who aim to spread information using several social media. In the project on the residential smart grid, we design an intervention targeting improved sustainability. We develop a social energy app to teach and engage people in efficient practices. In data centers, a better understanding is needed of the interplay between computation and energy consumption, before interventions can be proposed. Our results are a step towards such better understanding.In the final project, given a Web crawl, we first show how the underlying distributions in this complex system differ between malicious and clean websites. Then we demonstrate how such knowledge can support detecting malware-affected websites. We conclude this dissertation by presenting a systematic overview and lessons learned from the data science process undertaken in each project

CIVIS-YouPower

This is the source code of the YouPower app as part of CIVIS project

Mobile Phone Call Data as a Regional Socio-economic Proxy Indicator

VK: Ylä-Jääski, A.The advent of publishing anonymized call detail records opens the door for temporal and spatial human dynamics studies. Such studies, besides being useful for creating universal models for mobility patterns, could be also used for creating new socio-economic proxy indicators that will not rely only on the local or state institutions. In this paper, from the frequency of calls at different times of the day, in different small regional units (sub-prefectures) in Côte d'Ivoire, we infer users' home and work sub-prefectures. This division of users enables us to analyze different mobility and calling patterns for the different regions. We then compare how those patterns correlate to the data from other sources, such as: news for particular events in the given period, census data, economic activity, poverty index, power plants and energy grid data. Our results show high correlation in many of the cases revealing the diversity of socio-economic insights that can be inferred using only mobile phone call data. The methods and the results may be particularly relevant to policy-makers engaged in poverty reduction initiatives as they can provide an affordable tool in the context of resource-constrained developing economies, such as Côte d'Ivoire's.Peer reviewe

Proceedings of 2016 European Intelligence and Security Informatics Conference (EISIC)

This short empirical paper investigates a snapshotof about two million files from a continuously updated bigdata collection maintained by F-Secure for security intelligencepurposes. By further augmenting the snapshot with open datacovering about a half of a million files, the paper examines twoquestions: (a) what is the shape of a probability distributioncharacterizing the relative share of malware files to all filesdistributed from web-facing Internet domains; and (b) what is thedistribution shaping the popularity of malware files? A bimodaldistribution is proposed as an answer to the former question,while a graph theoretical definition for the popularity conceptindicates a long-tailed, extreme value distribution. With these twoquestions – and the answers thereto, the paper contributes to theattempts to understand large-scale characteristics of malware atthe grand population level – at the level of the whole Internet.</p