Purpose: The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. Design/methodology/approach: A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. Findings: The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods. Originality/value: This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

There is a debate in Argentina about the effectiveness of mandatory lockdown policies containing severe acute respiratory syndrome coronavirus type 2 disease. This policy has already 6 months long making it one of the longest in the world. The population effort to comply with the lockdown has been decreasing over time given the economic and social costs that it entails. This contribution analyzes the relationship between mobility and contagion in Argentina at a provincial level. It also models issues of internal political discussion on regional contagion and the effect of protests and unexpected crowd events. I use pool, fixed, and random effects panel data modeling and results show that lockdown in Argentina has been effective in reducing mobility but not in a way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and but extend it without complementary mitigation measures it losses effectiveness. The contagion rate seems to be discretely displaced in time and resurges amidst slowly increasing in mobility.

We describe a case of web scraping data-based price indices implemented in a mid-size city in a moderate inflationary country. Full consumer price (CPI) and construction cost (CCI) indices were implemented for an entire city obtaining efficient results compared to statistics using traditional data collection methods. We state that web scraping combined with big data techniques will allow estimating more individualized and efficient metrics comparable in quality to official statistics. Web scraping technologies empower civil society and small research groups alike by allowing them gather and interpret socioeconomic data. It also helps to create new dimensions of analysis by allowing changes in frequency and focus on specific groups of products and services.

Purpose: This paper aims to provide information about the structure of collaborative work among Argentinian economics. The study provides specific applied research of social network analysis focus on this profession in this specific country. Design/methodology/approach: The contribution opted for applying social network analysis tools to papers presented in a congress and published in its proceedings. The authors focus in detecting main actors, groups of co-authorship, professionals acting as bridges between groups and differences between genders. Findings: The paper provides empirical insights about how co-authorship has evolved between Argentine economists. The authors find that structural properties of the network, main actors, both male and female, main universities or center that affiliates them, a gender gap that might be closing out. Research limitations/implications: The paper focuses on the network for the period 1964-2014 without a more detailed dynamic. It also does not explain main topics worked by the authors. Practical implications: The work provides knowledge about how groups are created in Economics in Argentina, how cooperation has evolved and what has been the role of women in this development. It also shows how different departments and entities collaborate with diverse success in the creation of new knowledge in Economics in Argentina. Originality/value: The paper works with data from a source of information non-previously studied and contributes in explaining a particular type of collaborative work in a profession in Argentina.

Argentina has long been a tea provider with diverse types and variants dispatched to the internal market and abroad. The drink and its variants are a popular complement to other infusions such as yerba mate and coffee. We scraped an extensive online price database for analyzing the tea products offered in local supermarkets. We use a panel data modeling approach and test if attributes are associated with pricing and price discounting in the retail tea market. We control the estimations for time effects and exchange rate metrics because of the unstable macroeconomics. Time effects are highly significant and robust across estimations: we detect weekly, seasonal patterns, and yearly cycles. Attributes such as brand and variety play a weaker role in pricing but a stronger one in promotions.

Facebook pages (FP) are meeting points between events and actors in virtual social networks (RSV) of this platform. Companies and events bring together actors that participate through the online expression mechanisms that are enabled, through reactions, tagging, photo posting, liking, comments, etc. These interactions form online contact networks. We propose to investigate structural variables of the networks captured online with structural variables of these networks. We found that various structural variables related to concentration or disconnection between nodes are negatively associated with popularity measured by the number of likes, on the other hand, the most popular FPs associated with the media (radio and news).