Purpose Despite the quick spread of the use of mobile devices in survey participation, there is still little knowledge about the potentialities and challenges that arise from this increase. The purpose of this paper is to study how respondents’ preferences drive their choice of a certain device when participating in surveys. Furthermore, this paper evaluates the tolerance of participants when specifically asked to use mobile devices and carry out other specific tasks, such as taking photographs. Design/methodology/approach Data were collected by surveys in Spain, Portugal and Latin America by Netquest, an online fieldwork company. Findings Netquest panellists still mainly preferred to participate in surveys using personal computers. Nevertheless, the use of tablets and smartphones in surveys showed an increasing trend; more panellists would prefer mobile devices, if the questionnaires were adapted to them. Most respondents were not opposed to the idea of participating in tasks such as taking photographs or sharing GPS information. Research limitations/implications The research concerns an opt-in online panel that covers a specific area. For probability-based panels and other areas the findings may be different. Practical implications The findings show that online access panels need to adapt their surveys to mobile devices to satisfy the increasing demand from respondents. This will also allow new, and potentially very interesting data collection methods. Originality/value This study contributes to survey methodology with updated findings focusing on a currently underexplored area. Furthermore, it provides commercial online panels with useful information to determine their future strategies.

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In the era of Big Data, the Internet has become one of the main data sources: Data can be collected for relatively low costs and can be used for a wide range of purposes. To be able to timely support solid decisions in any field, it is essential to increase data production efficiency, data accuracy, and reliability. In this framework, our paper aims at identifying an optimized and flexible method to collect and, at the same time, geolocate social media information over a whole country. In particular, the target of this paper is to compare three alternative methods to collect data from the social media Twitter. This is achieved considering four main comparison criteria: Collection time, dataset size, pre-processing phase load, and geographic distribution. Our findings regarding Great Britain identify one of these methods as the best option, since it is able to collect both the highest number of tweets per hour and the highest percentage of unique tweets per hour. Furthermore, this method reduces the computational effort needed to pre-process the collected tweets (e.g., showing the lowest collection times and the lowest number of duplicates within the geographical areas) and enhances the territorial coverage (if compared to the population distribution). At the same time, the effort required to set up this method is feasible and less prone to the arbitrary decisions of the researcher.

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This paper proposes an ex-post comparison of portfolio selection strategies. These are applied to certain preselected assets among about ten thousand stocks on the global market. In particular, we preselected a few assets for each portfolio selection problem, taking into account different return characteristics. The preselecting criteria take into account the joint Markovian behavior of the returns; furthermore, they consider the assets who optimize the association with market stochastic bounds, having the highest ex-ante reward-risk performance. The results obtained with different pre-selection criteria are merged in order to identify assets with common characteristics which are appealing for investors. The impact of assets pre-selection on the portfolio choices is also studied. In particular, we compare the performance of different strategies that use or do not use the preselecting criteria. We finally propose the comparison of the ex-post final wealth obtained with the optimization of several reward-risk functionals that use the stochastic bounds of the preselected assets. For every comparison, we assume that the returns follow a non-parametric Markov chain, where the investors recalibrate their portfolios on a weekly basis.

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In the era of social media, the huge availability of digital data (e.g. posts sent through social networks or unstructured data scraped from websites) allows to develop new types of research in a wide range of fields. These types of data are characterized by some advantages such as reduced collection costs, short retrieval times and production of almost real-time outputs. Nevertheless, their collection and analysis can be challenging. For example, particular approaches are required for the selection of posts related to specific topics; moreover, retrieving the information we are interested in inside Twitter posts can be a difficult task.The main aim of this paper is to propose an unsupervised dictionary-based method to filter tweets related to a specific topic, i.e. environment. We start from the tweets sent by a selection of Official Social Accounts clearly linked with the subject of interest. Then, a list of keywords is identified in order to set a topic-oriented dictionary. We test the performance of our method by applying the dictionary to more than 54 million geolocated tweets posted in Great Britain between January and May 2019.

In current times Internet and social media have become almost unavoidabletools to support research and decision making processes in various fields.Nevertheless, the collection and use of data retrieved from these types ofsources pose different challenges. In a previous paper we compared theefficiency of three alternative methods used to retrieve geolocated tweets overan entire country (United Kingdom). One method resulted as the bestcompromise in terms of both the effort needed to set it and quantity/quality ofdata collected. In this work we further check, in term of content, whether thethree compared methods are able to produce “similar information”. Inparticular, we aim at checking whether there are differences in the level ofsentiment estimated using tweets coming from the three methods. In doing so,we take into account both a cross-section and a longitudinal perspective. Ourresults confirm that our current best option does not show any significantdifference in the sentiment, producing globally scores in between the scoresobtained using the two alternative methods. Thus, such a flexible and reliablemethod can be implemented in the data collection of geolocated tweets in othercountries and for other studies based on the sentiment analysis.