Exploring digital yesterdays - reflections on new media and the future of communication history

"This paper emanates from the consideration that communication history cannot only focus on communication that is within today's past but must also to cope with challenges communication history will face in 'tomorrow's yesterdays'. In nowadays perspective, apparent challenges for the future of communication historiography are posed by the impact of (now) new media technologies and digitalization. The article reflects about different shifts digitalization may bring for communication historiography, in terms of digital media as sources and the impact of digital communication on the understanding of temporal and spatial relations in communication historiography. Doing so, the paper discusses from a communication studies perspective if 'new media' history likewise entails a new 'media history'. The article concludes that digital media will prompt communication historians to adapt to new conditions. Such adaption to the respective 'new' is depicted as constituent of historical research as communication history has ever been kind of change management." (author's abstract

An investigation of estimation performance for a multivariate Poisson-gamma model with parameter dependency

Statistical analysis can be overly reliant on naive assumptions of independence between different data generating processes. This results in having greater uncertainty when estimating underlying characteristics of processes as dependency creates an opportunity to boost sample size by incorporating more data into the analysis. However, this assumes that dependency has been appropriately specified, as mis-specified dependency can provide misleading information from the data. The main aim of this research is to investigate the impact of incorporating dependency into the data analysis. Our motivation for this work is concerned with estimating the reliability of items and as such we have restricted our investigation to study homogeneous Poisson processes (HPP), which can be used to model the rate of occurrence of events such as failures. In an HPP, dependency between rates can occur for numerous reasons. Whether it is similarity in mechanical designs, failure occurrence due to a common management culture or comparable failure count across machines for same failure modes. Multiple types of dependencies are considered. Dependencies can take different forms, such as simple linear dependency measured through the Pearson correlation, rank dependencies which capture non-linear dependencies and tail dependencies where the strength of the dependency may be stronger in extreme events as compared to more moderate one. The estimation of the measure of dependency between correlated processes can be challenging. We develop the research grounded in a Bayes or empirical Bayes inferential framework, where uncertainty in the actual rate of occurrence of a process is modelled with a prior probability distribution. We consider prior distributions to belong to the Gamma distribution given its flexibility and mathematical association with the Poisson process. For dependency modelling between processes we consider copulas which are a convenient and flexible way of capturing a variety of different dependency characteristics between distributions. We use a multivariate Poisson – Gamma probability model. The Poisson process captures aleatory uncertainty, the inherent variability in the data. Whereas the Gamma prior describes the epistemic uncertainty. By pooling processes with correlated underlying mean rate we are able to incorporate data from these processes into the inferential process and reduce the estimation error. There are three key research themes investigated in this thesis. First, to investigate the value in reducing estimation error by incorporating dependency within the analysis via theoretical analysis and simulation experiments. We show that correctly accounting for dependency can significantly reduce the estimation error. The findings should inform analysts a priori as to whether it is worth pursuing a more complex analysis for which the dependency parameter needs to be elicited. Second, to examine the consequences of mis-specifying the degree and form of dependency through controlled simulation experiments. We show the relative robustness of different ways of modelling the dependency using copula and Bayesian methods. The findings should inform analysts about the sensitivity of modelling choices. Third, to show how we can operationalise different methods for representing dependency through an industry case study. We show the consequences for a simple decision problem associated with the provision of spare parts to maintain operation of the industry process when depenency between event rates of the machines is appropriately modelled rather than being treated as independent processes.Statistical analysis can be overly reliant on naive assumptions of independence between different data generating processes. This results in having greater uncertainty when estimating underlying characteristics of processes as dependency creates an opportunity to boost sample size by incorporating more data into the analysis. However, this assumes that dependency has been appropriately specified, as mis-specified dependency can provide misleading information from the data. The main aim of this research is to investigate the impact of incorporating dependency into the data analysis. Our motivation for this work is concerned with estimating the reliability of items and as such we have restricted our investigation to study homogeneous Poisson processes (HPP), which can be used to model the rate of occurrence of events such as failures. In an HPP, dependency between rates can occur for numerous reasons. Whether it is similarity in mechanical designs, failure occurrence due to a common management culture or comparable failure count across machines for same failure modes. Multiple types of dependencies are considered. Dependencies can take different forms, such as simple linear dependency measured through the Pearson correlation, rank dependencies which capture non-linear dependencies and tail dependencies where the strength of the dependency may be stronger in extreme events as compared to more moderate one. The estimation of the measure of dependency between correlated processes can be challenging. We develop the research grounded in a Bayes or empirical Bayes inferential framework, where uncertainty in the actual rate of occurrence of a process is modelled with a prior probability distribution. We consider prior distributions to belong to the Gamma distribution given its flexibility and mathematical association with the Poisson process. For dependency modelling between processes we consider copulas which are a convenient and flexible way of capturing a variety of different dependency characteristics between distributions. We use a multivariate Poisson – Gamma probability model. The Poisson process captures aleatory uncertainty, the inherent variability in the data. Whereas the Gamma prior describes the epistemic uncertainty. By pooling processes with correlated underlying mean rate we are able to incorporate data from these processes into the inferential process and reduce the estimation error. There are three key research themes investigated in this thesis. First, to investigate the value in reducing estimation error by incorporating dependency within the analysis via theoretical analysis and simulation experiments. We show that correctly accounting for dependency can significantly reduce the estimation error. The findings should inform analysts a priori as to whether it is worth pursuing a more complex analysis for which the dependency parameter needs to be elicited. Second, to examine the consequences of mis-specifying the degree and form of dependency through controlled simulation experiments. We show the relative robustness of different ways of modelling the dependency using copula and Bayesian methods. The findings should inform analysts about the sensitivity of modelling choices. Third, to show how we can operationalise different methods for representing dependency through an industry case study. We show the consequences for a simple decision problem associated with the provision of spare parts to maintain operation of the industry process when depenency between event rates of the machines is appropriately modelled rather than being treated as independent processes

Commemorative populism in the COVID-19 pandemic: the strategic (ab)use of memory in anti-Corona protest communication on telegram

During the COVID-19 pandemic, self-proclaimed resistance movements have organized protests against containment measures both in digital media and on the streets. References to the past and an invocation of collective memory have been important elements in the toolbox of their populist communication. We propose the notion of “commemorative populism” to describe the weaponization of history and memory for the proliferation of a political cause by populist activists. In a qualitative content analysis, we examined postings by the German “Querdenker,” a movement against Corona containment policies. Findings show 6 types of the (ab)use of history and collective memory: (1) the recontextualization of quotations by historical personalities, (2) the creation of false historical analogies and flattering genealogies, (3) the claim of historical exceptionalism, (4) the denigration of elites by referring to failures of medical history, (5) the dissemination of disinformation about historical facts, and (6) the support of conspiracy myths by the myths’ own history

Neither friend, nor device: the role of personal epistemologies in communication with smart speakers

With smart speakers diffusing into society, artificial intelligence is moving from the imaginative reservoirs of dystopian storytelling into vernacular living. How do users perceive communication with it? Are Alexa and Siri considered simple devices, sentient assistants, or even artificial friends? Based on nine qualitative interviews with former smart speaker users in Germany, this study analyzes smart speaker use and related personal epistemologies within a media repertoire perspective. By presenting six interrelated action-guiding principles explaining smart speaker use and people’s ambivalent sensemaking, we argue that smart speakers appear neither as friends nor as mere neutral devices to their users. The identified principles explain the peripheral role of smart speakers within media repertoires as handy but suspicious gimmicks. For future smart speaker adoption, whether smart assistants are interpreted as simple-minded, exploitative gimmicks or relevant, reliable, and trustworthy companions will be crucial

SUBJECT: FLUX ESTIMATION AND EMISSION OF METHYL ISOTHIOCYANATE RESULTING FROM A SMALL PLOT APPLICATION OF DAZOMET IN

an application of dazomet. In total, 452 pounds (205 kg) BasamidG ® were applied by granular spreader to a fallow, 1.0175 acre (0.4118 ha) plot at Plant Sciences, Inc., Nakano Complex, Watsonville, California. Application began October 18, 2006 at 08:45. It took 2 hours 15 minutes to complete, and a sprinkler system was activated approximately 30 minutes after the application finished to incorporate the pesticide into the soil. This first irrigation session lasted 3 hours 15 minutes; intermittent watering continued over the next few days. Air monitoring with 8 receptors (numbered 1 through 8) began 12 hours before application for a background sample and continued for 6 consecutive days and a total of 16 sampling periods. Air monitoring for period 1 began at about 8:45, coincident with the application. Data for the 17th and 18th sampling periods were collected 5 days after the 16th period. Parakrama (Gura) Gurusinghe will report on this study and its results in greater detail. Tammy Roush conducted modeling and back-calculation of the flux rates following methods established by Johnson et al. (1999). The Industrial Source Complex Short Term (ISCST3) (U.S. EPA 1995) was used to model the application. Pam Wofford and Tammy used WEATH6 to convert data recorded by the Department of Pesticide Regulation’s (DPR’s) weather statio

Academic traditions in communication: Expanding the field and redrawing the boundaries. ECREA 2018 special panel report

The main conference topic for the ECREA conference in Lugano addressed the many ways in which centers, cores and peripheries, and also mainstreams and alternatives are dealt with in academic media and communication research. The premise of this panel was to apply this general outline of the conference for academic introspection and to recount the many shifting centers and peripheries of communication research over time and discuss the redrawing of the contours of our expanding field. Less than two decades back, the question what the main subjects of media and communication inquiry were would have highlighted the centrality of (already slowly declining) mass media as the main pillars alongside journalism and public communication. Since then, the very notion of mediated communication has become less clear and has expanded to nearly all areas of the human experience and encompasses a variety of technologies, tools, platforms and intermediaries for communication

Recordings of digital media life: Advancing (qualitative) media diaries as a method

In times of digitalization, analyzing the highly complex media practices and mediated life worlds of individuals has become highly challenging, both in theoretical and methodological terms. From an empirical point of view, diary methods, and particularly qualitative (media) diaries, bear a great potential to gain access to these media practices and analyze them within the contexts of people’s everyday lives. In this article, we propose that it is fruitful to apply the characteristics of real diaries to research settings and consider them when designing diary studies as a researcher. Doing so can help to collect more “genuine” data and get a more holistic and adequate picture of digital media life. These characteristics comprise: (1) authenticity and naturalness, (2) autonomy in design, (3) multimodality and materiality, (4) intrinsic motivation, (5) functionalities of diary keeping, (6) continuity and periodicity, as well as (7) inferences about cultural and social conditions. We provide suggestions for implementing these characteristics in qualitative diary studies, and discuss the empirical challenges accompanying this approach