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What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970-2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship.

Based on a meta-analysis, this paper highlights the strength and relevance of several social incentive factors concerning pro-environmental behaviors, including social influence, network factors (like network size, network connection and leadership), trust in others, and trust in institutions. Firstly, our results suggest that social influence is necessary for the emergence of pro-environmental behaviors. More specifically, an internal social influence (i.e., motivating people to change their perceptions and attitudes) is essential to promote pro-environmental behaviors. Secondly, network connection encourages pro-environmental behaviors, meaning that the effectiveness of a conservation policy can be improved if connections among individuals are increased. Finally, trust in institutions can dictate individual behaviors to shape policy design and generate desired policy outcomes.

Troll internet messages, especially those posted on Twitter, have recently been recognised as a very powerful weapon in hybrid warfare. Hence, an important task for the academic community is to provide a tool for identifying internet troll accounts as quickly as possible. At the same time, this tool must be highly accurate so that its employment will not violate people's rights and affect the freedom of speech. Though such a task can be effectively fulfilled on purely linguistic grounds, as of yet, very little work has been done that could help to explain the discourse-specific features of this type of writing. In this paper, we suggest a quantitative measure for identifying troll messages which is based on taking into account certain sociolinguistic limitations of troll speech, and discuss two algorithms that both require as few as 50 tweets to establish the true nature of the tweets, whether 'genuine' or 'troll-like'.

Understanding the patterns and underlying mechanisms that come into play when employees exchange their knowledge is crucial for their work performance and professional development. Although much is known about the relationship between certain global network properties of knowledge-flow networks and work performance, less is known about the emergence of specific global network structures of knowledge flow. The paper therefore aims to identify a global network structure in blockmodel terms within an empirical knowledge-flow network and discuss whether the selected local network mechanisms are able to drive the network towards the chosen global network structure. Existing studies of knowledge-flow networks are relied on to determine the local network mechanisms. Agent-based modelling shows the selected local network mechanisms are able to drive the network towards the assumed hierarchical global structure. Nasl. z nasl. zaslona. Opis vira z dne 17. 2. 2021. Soavtorji: Anuška Ferligoj, Miha Škerlavaj, Aleš Žiberna. Bibliografija: str. 21-23. Abstract. ARRS, P5 -0168, SI, Družboslovna metodologija, statistika in informatika ARRS, P5-0410, SI, Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe ARRS, SI, Bločno modeliranje večnivojskih in časovnih omrežij

The relationship between age and well-being is U-shaped. One recent explanation for this empirical pattern is related to unmet aspirations theory, pointing out that optimism bias decreases life satisfaction at younger ages, whereas pessimism bias increases it at later stages of life. This paper investigates the effects of Internet use on subjective well-being over the life cycle. Our model investigates the proposition that Internet use affects aspirations, and that this effect is relatively stronger at younger and older ages. To investigate moderation effects of Internet use on the U-shape of life, we use the Eurobarometer annual surveys for the years 2010 to 2016, which provide rich information for around 150,000 individuals in all European countries. We focus on the EU Digital Agenda policy program, and exploit exogenous variation in broadband Internet take-up across European countries to identify the causal effects of Internet on life satisfaction for different age groups. The results of 2SLS estimations for a recursive bivariate ordered probit model show that active Internet users have a different well-being pattern over the life cycle compared to less active users. Specifically, we find that Internet use makes the U-shape of life steeper. Country-level evidence on aspiration levels for different demographic and Internet user groups indicates that our empirical results are consistent with unmet aspirations theory.

In this paper, we study the effect of verbalizing affective pictures on affective state and language production. Individuals describe (Study I: Spoken Descriptions of Pictures) or passively view (Study II: Passively Viewing Pictures) 40 pictures for the International Affective Picture System (IAPS) that gradually increase from neutral to either positive or negative content. We expected that both methods would result in successful affect induction, and that the effect would be stronger for verbally describing pictures than for passively viewing them. Results indicate that speakers indeed felt more negative after describing negative pictures, but that describing positive (compared to neutral) pictures did not result in a more positive state. Contrary to our hypothesis, no differences were found between describing and passively viewing the pictures. Furthermore, we analysed the verbal picture descriptions produced by participants on various dimensions. Results indicate that positive and negative pictures were indeed described with increasingly more affective language in the expected directions. In addition to informing our understanding of the relationship between (spoken) language production and affect, these results also potentially pave the way for a new method of affect induction that uses free expression.

Giving more to those who need more has an intuitive appeal for determining the just allocation of resources. The need principle is considered one of the three major principles of distributive justice. In contrast to equality or equity, however, evidence on the adherence to the needs principle rests mainly on stated instead of revealed preferences. In this paper we present an experimental design that exogenously assigns objective, heterogeneous need thresholds to individuals in small laboratory societies structured by a three-line network. The data reveal that a large proportion of individuals respond to others' need thresholds, but at a declining rate as thresholds increase. The equal distribution marks a discrete drop in the need satisfaction rate: Need thresholds above the equal distribution are less frequently satisfied. We conclude that others' needs are weighed against self-interest and equality. Our results provide evidence that distributions may be socially justified on grounds of the need principle.

The paper explores the travelling behaviour of migrant groups using Facebook audience estimates. Reduced geographical mobility is associated with increased risk of social exclusion and reduced socio-economic and psychological well-being. Facebook audience estimates are timely, openly available and cover most of the countries in the world. Facebook classifies its users based on multiple attributes such as the country of their previous residence, and whether they are frequent travellers. Using these data, we modelled the travelling behaviour of Facebook users grouped by countries of previous and current residence, gender and age. We found strong indications that the frequency of travelling is lower for Facebook users migrating from low-income countries and for women migrating from or living in countries with high gender inequality. Such mobility inequalities impede the smooth integration of migrants from low-income countries to new destinations and their well-being. Moreover, the reduced mobility of women who have lived or currently live in countries with conservative gender norms capture another aspect of the integration which is referring to socio-cultural norms and gender inequality. However, to provide more solid evidence on whether our findings are also valid for the general population, collaboration with Facebook is required to better understand how the data is being produced and pre-processed.

In this paper, a general class of estimators is proposed for estimating the finite population mean for sensitive variable, in the presence of measurement error and non-response in simple random sampling. Expressions for bias and mean square error up to first order of approximation, are derived. Impact of measurement errors is examined using real data sets, including the survey conducted at Quaid-i-Azam University, Islamabad. Simulated data sets are also used to observe the performance of the proposed estimators in comparison to some other estimators. We obtain the empirical bias and MSE values for the proposed and the competing estimators.

In this paper, we propose a new approach for learning node embeddings for weighted undirected networks. We perform a random walk on the network to extract the latent structural information and perform node embedding learning under a similarity-based framework. Unlike previous works, we apply a different criterion to capture the proximity information between nodes in a network, and use it for improved modeling of similarities between nodes. We show that the mean commute time (MCT) between two nodes, defined as the average time a random walker takes to reach a target node and return to the source, plays a crucial role in quantifying the actual degree of proximity between two nodes of the network. We then introduce a novel definition of a similarity matrix that is based on the pair-wise mean commute time captured, which enables us to adequately represent the connection of similar nodes. We utilize pseudoinverse of the Laplacian matrix of the graph for calculating such a proximity measure, capturing rich structural information out of the graph for learning more adequate node representations of a network. The results of different experiments on three real-world networks demonstrate that our proposed method outperforms existing related efforts in classification, clustering, visualization as well as link prediction tasks.

Data Availability: The complete dataset for the High Order Profile Expansion experiments has been published in the public repository: https://doi.org/10.6084/m9.figshare.9798155. [Abstract] Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms in Recommender Systems, especially in e-commerce, considering their good results. However, when the information is extremely sparse, independently of the domain nature, they do not present such good results. In particular, it is difficult to offer recommendations which are accurate enough to a user who has just arrived to a system or who has rated few items. This is the well-known new user problem, a type of cold-start. Profile Expansion techniques had been already presented as a method to alleviate this situation. These techniques increase the size of the user profile, by obtaining information about user tastes in distinct ways. Therefore, recommender algorithms have more information at their disposal, and results improve. In this paper, we present the High Order Profile Expansion techniques, which combine in different ways the Profile Expansion methods. The results show 110% improvement in precision over the algorithm without Profile Expansion, and 10% improvement over Profile Expansion techniques. Ministerio de Economía y Competitividad; TIN2015-70648-P Xunta de Galicia; ED431G/01 2016-2019

Information about a person's income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person's income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6-16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent.

TV series represent a growing sector of the entertainment industry. Being able to predict their performance allows a broadcasting network to better focus the high investment needed for their preparation. In this paper, we consider a well known TV series-The Big Bang Theory-to identify factors leading to its success. The factors considered are mostly related to the script, such as the characteristics of dialogues (e.g., length, language complexity, sentiment), while the performance is measured by the reviews submitted by viewers (namely the number of reviews as a measure of popularity and the viewers' ratings as a measure of appreciation). Through correlation and regression analysis, two sets of predictors are identified respectively for appreciation and popularity. In particular the episode number, the percentage of male viewers, the language complexity and text length emerge as the best predictors for popularity, while again the percentage of male viewers and the language complexity plus the number of we-words and the concentration of dialogues are the best choice for appreciation.

The existing literature fails to identify to which extent the utilization of social media could be relevant for increasing the effectiveness of knowledge management, in respect to overall business operations. In order to shed some light on this area we define three goals. Firstly, we investigate to what extent the different activities of clients on social media (SM), are important to the processes of knowledge management (KM) in companies. Secondly, we examine to what extent KM functions can be relevant in attaining the quality of IT services. Thirdly, we analyze to what extent KM mediates between SM and the quality of IT services, that is, which client activities on SM should be formalised in the form of KM processes so as to influence the quality of IT services. In order to asses these goals, the decision tree method was used at the sample of B2B companies, more specifically at the sample of those companies offering Knowledge Intensive Business Services (KIBS). The study has shown that: (i) SM client activities have the largest importance for building efficient KM ; (ii) KM functions are relevante to the overall quality of IT services, and (iii) those SM options and activities have been identified, whose importance for the assessment of the quality of IT services is indirectly transmitted through KM. This paper offers new empirical evidence which can lead to a better understanding of the role of KM in KIBS. Thanks to the obtained findings, managers will be able to define the goals of their companies in relation to the utilization of SM, more specifically: their presentation on SM, monitoring the outcomes of the SM, usage improving their KM practices and, thus, define strategies to increase the quality of the IT services they offer.

This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area-proportional Euler diagrams but linear diagrams can exploit length-proportional straight-lines for the same purpose. Another common approach is to use numerical annotations. We first conducted two empirical studies, one on Euler diagrams and the other on linear diagrams. These suggest that area-proportional Euler diagrams with numerical annotations and length-proportional linear diagrams without numerical annotations support significantly better task performance. We then conducted a third study to investigate which of these two notations should be used in practice. This suggests that area-proportional Euler diagrams with numerical annotations most effectively supports task performance and so should be used to visualize set cardinalities. However, these studies focused on data that can be visualized reasonably accurately using circles and the results should be taken as valid within that context. Future work needs to determine whether the results generalize both to when circles cannot be used and for other ways of encoding cardinality information.

This paper investigates the relationship between candidates' online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan's 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates' Facebook posts will be positively related to their election results, I calculated each candidate's Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate's Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election.

Participative branding is a process by which DMOs, locals and visitors contribute, through their activity in social media, to the definition of the emotional values associated to a destination. From the communicative point of view it is particularly important that DMOs and local citizens work together in the transmission of a set of coherent values that compose a personalised identity. This paper presents a novel methodology, based on semantic similarity measures, that permits to make an automated analysis of the emotional values transmitted by official tourist offices and by local citizens through social media. A study of 54,000 tweets from 9 main European tourist destinations highlights the lack of a strategy towards the communication of a distinctive brand and a strong gap between the official view transmitted by the DMOs of the destinations and the one communicated by their residents.