What you tweet is what we get?

Twitter has a high presence in our modern society, media and science. Numbers of studies with Twitter data – not only in communication research – show that tweets are a popular data source for science. This popularity can be explained by the mostly free data and its technically high availability, as well as the distinct and open communication structure. Even though much research is based on Twitter data, it is only suitable for research to a limited extent. For example, some studies have already revealed that Twitter data has a low explanatory power when predicting election outcomes. Furthermore, the rise of automated communication by bots is an urgent problem of Twitter data analysis. Although critical aspects of Twitter data have already been discussed to some extent (mostly in final remarks of studies), comprehensive evaluations of data quality are relatively rare.To contribute to a deeper understanding of problems regarding the scientific use of Twitter data leading to a more deliberate und critical handling of this data, the study examines different aspects of data quality, usability and explanatory power. Based on previous research on data quality, it takes a critical look with the following four dimensions: availability and completeness, quality (regarding authenticity, reliability and interpretability), language as well as representativeness. Based on a small case study, this paper evaluates the scientific use of Twitter data by elaborating problems in data collection, analysis and interpretation. For this illustrative purpose, the author typically gathered data via Twitter’s Streaming APIs: 73,194 tweets collected between 20–24/02/2017 (each 8pm) with the Streaming APIs (POST statuses/filter) containing the search term “#merkel”.Concerning data availability and completeness, several aspects diminish data usability. Twitter provides two types of data gateways: Streaming APIs (for real-time data) and REST APIs (for historical data). Streaming APIs only have a free available Spritzer bandwidth, that is limited to only one percent of the overall (global) tweet volume at any given time. This limit is a prevalent problem when collecting Twitter data to major events like elections and sports. The REST APIs do not usually provide data older than seven days. Furthermore, Twitter gives no information about the total or search term-related tweet volume at any time.In addition to incomplete data, several quality related aspects complicate data gathering and analysis, like the lack of user specific and verified information (age, gender, location), inconsistent hashtag usage, missing conversational context or poor data/user authenticity. Geo data on Twitter is – if available at all – rarely correct and not useful for filtering relevant tweets. Searching and filtering relevant tweets by search terms can be deceptive, because not every tweet concerning a topic contains corresponding hashtags. Furthermore, it is difficult to find a perfect search term for broader and dynamically changing topics. Besides, the missing conversational context of tweets impedes interpretation of statements (especially with regard to irony or sarcasm). In addition, the rise of social bots diminishes dataset quality enormously. In the dataset generated for this work, only three of the top 30 accounts (by tweet count) could be directly identified as genuine. One fourth of all accounts in this dataset generated about 60% of all tweets. If the high-performing accounts predominantly consist of bots, the negative impact on data quality is immense.Another problem of Twitter analysis is Internet language. While Emojis can be misinterpreted, abbreviations, neologisms, mixed languages and a lack of grammar impede text analysis. In addition to low data quality in general, the quality of tweet content and its representativeness is crucial. This work compares user statistics with research articles on SCOPUS as well as media coverage of two selected, German quality newspapers. Twitter is – compared to its user count – enormously overrepresented in media and science. Only 16% of German adults (over 18 years) are monthly active (MAUs) and merely four percent are daily active users.Considering all presented problems, Twitter can be a good data source for research, but only to a limited extent. Researchers must consider that Twitter does not guarantee complete, reliable and representative data. Ignoring those critical points can mislead data analysis. While Twitter data can be suitable for specific case studies, like the usage and spread of selected hashtags or the twitter usage of specific politicians, you cannot use it for broader, nation-based surveys like the prediction of elections or the public opinion on a specific topic. Twitter has a low representativeness and is mostly an “elite medium” with an uncertain future (concerning the stagnating number of users and financial problems).     

Impact of Online Customer Reviews on Sales Outcomes: An Empirical Study Based on Prospect Theory

This study examines the influence of the valence of online customer reviews on sales outcomes based on prospect theory. Numerous studies have revealed the importance of customer reviews in online marketing. However, only few studies have explored the impact of online customer reviews on sales outcomes in the dynamic process. Prior studies in behavioral economics literature have indicated that people differently value gains and losses and that losses have more emotional impact than an equivalent amount of gains. This study verifies whether prospect theory applies to the relation between online customer reviews and sales outcomes. Relevant data were collected from Amazon.co.jp, and three statistical models were employed to investigate the relation between the two factors. Major findings confirm that negative customer reviews considerably impact online sales than positive reviews. Furthermore, the findings indicate that the marginal effects of positive and negative reviews decrease with the increase in their volume. The results of this study will enable marketers to compare the relative sales effects of different types of customer reviews and improve the effectiveness of customer service management.     

Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring

In this paper, a structural analysis of hybrid censoring models is presented. This new modularization approach to hybrid censoring models enables a convenient derivation of distributional results. For instance, it allows to derive the exact distribution of the MLEs under an exponential assumption for very complex hybrid scenarios. In order to illustrate the benefit of this idea, we apply it to four new unified progressive hybrid censoring schemes. They are extensions of already proposed unified Type-I/II/III/IV hybrid censoring schemes to progressively Type-II censored data. The resulting analysis shows that the modularization approach provides a powerful, efficient, and elegant tool to study even more complex hybrid censoring models.     

Inference for the two-parameter bathtub-shaped distribution based on record data

Here we consider the record data from the two-parameter of bathtub-shaped distribution. First, we develop simplified forms for the single moments, variances and covariance of records. These distributional properties are quite useful in obtaining the best linear unbiased estimators of the location and scale parameters which can be included in the model. The estimation of the unknown shape parameters and prediction of the future unobserved records based on some observed ones are discussed. Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems. The likelihood method, moment based method, bootstrap methods as well as the Bayesian sampling techniques are applied for the inference problems. The point predictors and credible intervals of future record values based on an informative set of records can be developed. Monte Carlo simulations are performed to compare the so developed methods and one real data set is analyzed for illustrative purposes.     

Stochastic comparisons of coherent systems

The study of stochastic comparisons of coherent systems with different structures is a relevant topic in reliability theory. Several results have been obtained for specific distributions. The present paper is focused on distribution-free comparisons, that is, orderings which do not depend on the component distributions. Different assumptions for the component lifetimes are considered which lead us to different comparison techniques. Thus, if the components are independent and identically distributed (IID) or exchangeable, the orderings are obtained by using signatures. If they are just ID (homogeneous components), then ordering results for distorted distributions are used. In the general case or in the case of independent (heterogeneous) components, a similar technique based on generalized distorted distributions is applied. In these cases, the ordering results may depend on the copula used to model the dependence between the component lifetimes. Some illustrative examples are included in each case.     

The multiple filter test for change point detection in time series

A framework for the detection of change points in the expectation in sequences of random variables is presented. Specifically, we investigate time series with general distributional assumptions that may show an unknown number of change points in the expectation occurring on multiple time scales and that may also contain change points in other parameters. To that end we propose a multiple filter test (MFT) that tests the null hypothesis of constant expectation and, in case of rejection of the null hypothesis, an algorithm that estimates the change points.The MFT has three important benefits. First, it allows for general distributional assumptions in the underlying model, assuming piecewise sequences of i.i.d. random variables, where also relaxations with regard to identical distribution or independence are possible. Second, it uses a MOSUM type statistic and an asymptotic setting in which the MOSUM process converges weakly to a functional of a Brownian motion which is then used to simulate the rejection threshold of the statistical test. This approach enables a simultaneous application of multiple MOSUM processes which improves the detection of change points that occur on different time scales. Third, we also show that the method is practically robust against changes in other distributional parameters such as the variance or higher order moments which might occur with or even without a change in expectation. A function implementing the described test and change point estimation is available in the R package MFT.     

Mapping global sustainability report scoring: a detailed analysis of Europe and Asia

This study maps the General Reporting Initiative (GRI) relations for a set of global enterprises. Enterprise interrelationships are built using the Mutual Information (MI) of the data reported by enterprises. From the MI topology, we describe interrelationships among the enterprises using chord diagrams to represent the inter- and intra-connectivity between geographical regions and economic sectors globally, by continent and country. Detailed maps are presented for European and Asian regions/sectors, including the relationship between Europe and Asia’s top reporting countries. Our findings reinforce previous research regarding the role of Europe as a driver of sustainability and its influence worldwide. We also determined that Spain is a major player in Europe and that Northern Europe does not have the assumed leading role mentioned in related studies. Eastern Asia is, by far, the leader of the region, and the GRI maps demonstrate the preponderant role of China in the region and the minimal role of India. MI topology maps outline the behavior of the economic sectors, for all the studied regions. Furthermore, a measure that relates the inter- to intra-connections is presented to describe the internal an external relationships among regions/sectors.     

Measuring populist discourse with semantic text analysis: an application on grassroots populist mobilization

Populism is a concept employed to qualify the political behavior of a large number of actors at a worldwide scale, with scientists classifying the latter into populists and non-populists according to dimensions such as ideology, strategy, discourse, economic policy, and even style. This article analyzes existing schools of thought on the nature of populism and argues that conceptualizing populism as a specific type of anti-elite discourse in the name of the People is both conceptually and methodologically the most coherent and useful way to understand the phenomenon. Additionally, it suggests discarding crude, dichotomous classification in favor of a gradated view of populist mobilization by means of quantifying populist discourse and observing its spatial and temporal variation. It adds value to current methods of measurement by demonstrating why and how clause-based semantic text analysis can provide optimal quantitative results while retaining qualitative elements for mixed-methods analysis. Aiming, moreover, at expanding the scope of populism studies by overcoming a narrow view that focuses exclusively at party system developments, it applies semantic text analysis to the study of grassroots mobilization during the Great Recession. Results point to the wide use of populist discourse on the part of movement activists seeking an inclusive language when framing disparate social grievances in a given constituency, a finding with important implications with regards to how populism can facilitate straddling the divide that purportedly distinguishes institutionalized party system behavior from the social movement milieu.     

A mixed-mode sensitive research on cannabis use and sexual addiction: improving self-reporting by means of indirect questioning techniques

In this article, we describe the methods employed and the results obtained from a mixed-mode “sensitive research” conducted in Spain to estimate certain aspects concerning patterns of cannabis consumption and sexual addiction among university students. Three different data-collection methods are considered and compared: direct questioning, randomized response technique and item sum technique. It is shown that posing direct questions to obtain sensitive data produces significantly lower estimates of the surveyed characteristics than do indirect questioning methods. From the analysis, it emerges that male students seem to be more affected by sex addiction than female students while for cannabis consumption there is no evidence of a predominant gender effect.