Diverging patterns of interaction around news on social media: insularity and partisanship during the 2018 Italian election campaign

ABSTRACT

The paper considers how social media ecologies are affecting partisan engagement around political news and online attention economies by investigating the case of the 2018 Italian general election. By analyzing Twitter and Facebook interactions around political news in the lead-up to the election, we shed light on levels of insularity characterizing sources preferred by different partisan communities and investigate how specific patterns of active attention emerge around different sources and around stories proposing different framing of specific political actors. Our findings indicate that, on Twitter, sources mainly shared by supporters of populist parties (the Five Star Movement and the League) are characterized by higher levels of insularity compared to those shared by supporters of other parties. We also find that, on Facebook, news items published by highly insular sources receive a higher number of shares per comment. Finally, our analyses show that news presenting a positive framing of the Five Star Movement – the unique ‘cyber party’ in the system – receives a higher number of shares per comment compared to items presenting the Movement in a negative light, while the opposite is true for stories on all other political parties.     

Biopharmaceutical and chemical firms’ R&D disclosure,and cost of equity: The impact of the regulatory regime

Based on the proprietary costs theory, this paper aims to survey whether the regulatory regime (mandatory versus voluntary) of research and development (R&D) narrative disclosures impacts, by the means of a reduced information asymmetry, on the cost of equity capital. In order to construct a disclosure index to investigate the extent and the comprehensiveness of R&D information, the methodology adopted was the content analysis of 77 biopharmaceutical and chemical listed companies’ management reports from eight Western European countries across the period 2005–2009. Hence, we obtained an (unbalanced) panel data of 309 observations. The cost of capital has subsequently been regressed on the disclosure index. Results confirm a larger amount of R&D disclosures whereas information is more regulated, but they do not confirm an inverse relation with the cost of capital.     

Spatial Distribution of Social Inequality in the Metropolitan District of Quito,Ecuador

Social Indicators Research - Exploring the spatial variation of regional social inequality allows the design of public policies; therefore, this study analyzes this phenomenon in the Metropolitan...     

What happens if you single out? An experiment

We present an experiment investigating the effects of singling out an individual on trust and trustworthiness. We find that (a) trustworthiness falls if there is a singled out subject; (b) non-singled out subjects discriminate against the singled out subject when they are not responsible of the distinct status of this person; (c) under a negative frame, the singled out subject returns significantly less; (d) under a positive frame, the singled out subject behaves bimodally, either selecting very low or very high return rates. Overall, singling out induces a negligible effect on trust but is potentially disruptive for trustworthiness.     

Knowledge Creation within Industrial Systems

Intangible resources are the source of competitive advantage in theknowledge economy. Knowledge and intellectual capital within firms havegathered worldwide attention; theories and methods for knowledgecreation management have been developed for organizations and alliances.Economic reality shows the importance of industrial districts becausesocial systems in districts provide dynamic contexts of multifacetedrelationships. These dynamic contexts or district ba (Japanesefor place) enable firms to create and harness knowledge that resides innetwork relationships.The paper develops a theoretical frame of four kinds of district ba thatsupport four knowledge processes. Organizational networks within andbetween different district ba are conceptualized. The creationof knowledge within district ba is used to analyze differentlevels of interaction between firms in the same or different districtsand in the same or different industries. The case study of the JETROlocal-to-local program between Ishikawa prefecture in Japan and Como andVarese in Italy is used to illustrate the theoretical concepts. Districtba are used to analyze the knowledge creation dynamics withinand between districts and to identify possible strategies for districtgovernance.     

Algebraic Markov Bases and MCMC for Two-Way Contingency Tables

ABSTRACT.  The Diaconis–Sturmfels algorithm is a method for sampling from conditional distributions, based on the algebraic theory of toric ideals. This algorithm is applied to categorical data analysis through the notion of Markov basis. An application of this algorithm is a non-parametric Monte Carlo approach to the goodness of fit tests for contingency tables. In this paper, we characterize or compute the Markov bases for some log-linear models for two-way contingency tables using techniques from Computational Commutative Algebra, namely Gröbner bases. This applies to a large set of cases including independence, quasi-independence, symmetry, quasi-symmetry. Three examples of quasi-symmetry and quasi-independence from Fingleton ( Models of category counts , Cambridge University Press, Cambridge, 1984) and Agresti ( An Introduction to categorical data analysis , Wiley, New York, 1996) illustrate the practical applicability and the relevance of this algebraic methodology.     

Alpha as Ambiguity: Robust Mean‐Variance Portfolio Analysis

We derive the analogue of the classic Arrow–Pratt approximation of the certainty equivalent under model uncertainty as described by the smooth model of decision making under ambiguity of Klibanoff, Marinacci, and Mukerji (2005). We study its scope by deriving a tractable mean‐variance model adjusted for ambiguity and solving the corresponding portfolio allocation problem. In the problem with a risk‐free asset, a risky asset, and an ambiguous asset, we find that portfolio rebalancing in response to higher ambiguity aversion only depends on the ambiguous asset's alpha, setting the performance of the risky asset as benchmark. In particular, a positive alpha corresponds to a long position in the ambiguous asset, a negative alpha corresponds to a short position in the ambiguous asset, and greater ambiguity aversion reduces optimal exposure to ambiguity. The analytical tractability of the enhanced Arrow–Pratt approximation renders our model especially well suited for calibration exercises aimed at exploring the consequences of model uncertainty on equilibrium asset prices.     

The bipolar Choquet integral representation

Cumulative Prospect Theory is the modern version of Prospect Theory and it is nowadays considered a valid alternative to the classical Expected Utility Theory. Cumulative Prospect theory implies Gain-Loss Separability, i.e., the separate evaluation of losses and gains within a mixed gamble. Recently, some authors have questioned this assumption of the theory, proposing new paradoxes where the Gain-Loss Separability is violated. We present a generalization of Cumulative Prospect Theory which does not imply Gain-Loss Separability and is able to explain the cited paradoxes. On the other hand, the new model, which we call the bipolar Cumulative Prospect Theory, genuinely generalizes the original Prospect Theory of Kahneman and Tversky, preserving the main features of the theory. We present also a characterization of the bipolar Choquet Integral with respect to a bi-capacity in a discrete setting.     

Using the Reversible Jump MCMC Procedure for Identifying and Estimating Univariate TAR Models

One way that has been used for identifying and estimating threshold autoregressive (TAR) models for nonlinear time series follows the Markov chain Monte Carlo (MCMC) approach via the Gibbs sampler. This route has major computational difficulties, specifically, in getting convergence to the parameter distributions. In this article, a new procedure for identifying a TAR model and for estimating its parameters is developed by following the reversible jump MCMC procedure. It is found that the proposed procedure conveys a Markov chain with convergence properties.