Inference to the Best Explanation in Uncertain Evidential Situations

It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by IBE** and JC, respectively, try to detect the bias of a coin while they are only partially certain what side the coin landed on. We show that IBE** more often prescribes high probability to the actual bias than JC. We also show that this happens considerably faster, that IBE** passes higher thresholds for high probability, and that it in general leads to more accurate probability distributions than JC

Dashboard of sentiment in Austrian social media during COVID-19

To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.Comment: 23 pages, 3 figures, 1 tabl

Unpacking polarization: Antagonism and Alignment in Signed Networks of Online Interaction

Online polarization research currently focuses on studying single-issue opinion distributions or computing distance metrics of interaction network structures. Limited data availability often restricts studies to positive interaction data, which can misrepresent the reality of a discussion. We introduce a novel framework that aims at combining these three aspects, content and interactions, as well as their nature (positive or negative), while challenging the prevailing notion of polarization as an umbrella term for all forms of online conflict or opposing opinions. In our approach, built on the concepts of cleavage structures and structural balance of signed social networks, we factorize polarization into two distinct metrics: Antagonism and Alignment. Antagonism quantifies hostility in online discussions, based on the reactions of users to content. Alignment uses signed structural information encoded in long-term user-user relations on the platform to describe how well user interactions fit the global and/or traditional sides of discussion. We can analyse the change of these metrics through time, localizing both relevant trends but also sudden changes that can be mapped to specific contexts or events. We apply our methods to two distinct platforms: Birdwatch, a US crowd-based fact-checking extension of Twitter, and DerStandard, an Austrian online newspaper with discussion forums. In these two use cases, we find that our framework is capable of describing the global status of the groups of users (identification of cleavages) while also providing relevant findings on specific issues or in specific time frames. Furthermore, we show that our four metrics describe distinct phenomena, emphasizing their independent consideration for unpacking polarization complexities

Colexification networks encode affective meaning

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42761-021-00033-1

Emotional talk about robotic technologies on Reddit : Sentiment analysis of life domains, motives, and temporal themes

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot (N = 3,433,554), AI (N = 2,821,614), automation (N = 879,092), bot (N = 21,559,939), intelligent agent (N = 15,119), and software agent (N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.publishedVersionPeer reviewe

Collective dynamics of multi-agent networks

Der zentrale wissenschaftliche Beitrag dieser Masterarbeit ist die Entwicklung und empirische Evaluation von IBE*, einer Synthese aus 'Jeffrey Conditionalization' und des 'Schlusses auf die beste Erklärung' (engl. 'Inference to the Best Explanation', IBE). Damit können auch Szenarien unsicherer Evidenz mit 'explanationist belief updates' erfasst werden. Es wird argumentiert, dass es fruchtbar ist, (probabilistische) Alternativen zu bayesianischer Inferenz zu untersuchen. Das 'Alien Die' Modell und das Konzept der Brier Punkte werden vorgestellt. In Simulationen mit vollständig sicherer Evidenz gelingt es, ein zentrales Result von Igor Douven zu replizieren: Der 'explanationist' ist schneller, fährt aber etwas höhere Brier Punkte ein. In Simulationen mit einer fixierten Unsicherheit der Evidenz ist der 'explanationist' wieder schneller und diesmal auch treffsicherer. Auch in Simulationen mit zufälliger Unsicherheit ist der 'explanationist' schneller und genauer: Wir finden damit eine entscheidende Schwachstelle des bayesianischen Agenten. IBE* scheint dem Problem der unsicheren Evidenz entgegenzuarbeiten. Es werden unterschiedliche Netzwerktopologien vorgestellt und unter verschiedenen Parametern visualisiert. Kollektive 'belief updates' werden auf diesen Netzwerken durchgeführt. Bei vollständiger Evidenz überschreitet der 'explanationist' den Schwellenwert bei jedem Netzwerktypen, im Gegensatz zum bayesianischen Agent. Wird die Evidenz unsicher, treten die Vorteile des 'explanationist' stärker hervor: Wir finden einen sehr großen Geschwindigkeitsvorteil. Dann werden Kontroversen bezüglich der Wahl der verwendeten Parameter und Konzepte in den Simulationen behandelt. Wir erarbeiten eine Definition von 'Computer Simulationen' und sprechen epistemologische Aspekte an. In einer Diskussion gehen wir auf das Potential von Simulationen für die Sozialwissenschaften ein. Schließlich erwähnen wir derzeitige Limitierungen unserer Arbeit und Möglichkeiten, die Forschung weiterzuführen.The main contribution of this thesis is the development and empirical evaluation of IBE*, a synthesis of Jeffrey Conditionalization and IBE (Inference to the Best Explanation) that generalizes explanationist updating to cases of uncertain evidence. It is argued that there are merits to be expected from studying (probabilistic) alternatives to Bayesian inference. The 'Alien Die' model and Brier scores are introduced. In simulations with full certainty of evidence, we succeed in replicating a recent key finding by Igor Douven: The explanationist is faster, but also incurs a slightly higher Brier score. In simulations with fixed (un-)certainty, the explanationist is again faster and also more accurate. Also in simulations with random uncertainty, the explanationist is the substantially faster and more accurate variant: We find a decisive shortcoming of the Bayesian approach. IBE* seems to be counteracting the problem of uncertain evidence. We introduce networks and visualize them under different parametrisation. We then run collective belief updates on these networks. With full certainty, in contrast to the Bayesian, the explanationist crosses the threshold for the true bias on all topologies. With uncertainty of evidence, this advantage is again more pronounced. We found a vast speed advantage of the explanationist. Then, controversies regarding the choice of specific parameters and concepts used in our simulations are addressed. We develop a definition of computer simulations and reflect on epistemological issues. We point out the potential of simulations for the social sciences. Finally, current limitations and further directions of our research are identified

Inference to the best explanation in uncertain evidential situations

It has recently been argued that a non-Bayesian probabilistic version of inference to the best explanation (IBE*) has a number of advantages over Bayesian conditionalization (Douven [2013]; Douven and Wenmackers [2017]). We investigate how IBE* could be generalized to uncertain evidential situations and formulate a novel updating rule IBE**. We then inspect how it performs in comparison to its Bayesian counterpart, Jeffrey conditionalization (JC), in a number of simulations where two agents, each updating by IBE** and JC, respectively, try to detect the bias of a coin while they are only partially certain what side the coin landed on. We show that IBE** more often prescribes high probability to the actual bias than JC. We also show that this happens considerably faster, that IBE** passes higher thresholds for high probability, and that it in general leads to more accurate probability distributions than JC. 1 Introduction 2 Generalizing Inference to the Best Explanation to Uncertain Evidential Situations 3 Detecting the Bias of a Coin 4 Overall Performance of IBE** versus Jeffrey Conditionalization 5 Speed of Convergence 6 The Threshold for High Subjective Probability 7 Epistemic Inaccuracy 8 Conclusion

Validating daily social media macroscopes of emotions

To study emotions at the macroscopic level, affective scientists have made extensive use of sentiment analysis on social media text. However, this approach can suffer from a series of methodological issues with respect to sampling biases and measurement error. To date, it has not been validated if social media sentiment can measure the day to day temporal dynamics of emotions aggregated at the macro level of a whole online community. We ran a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users and compare these with aggregated results of sentiment analysis of user discussions on the same online platform. Additionally, we preregistered a replication of our study using Twitter text as a macroscope of emotions for the same community. For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions, as well as between inter-day changes of both measurements. We further show that a combination of supervised and unsupervised text analysis methods is the most accurate approach to measure emotion aggregates. We illustrate the application of such social media macroscopes when studying the association between the number of new COVID-19 cases and emotions, showing that the strength of associations is comparable when using survey data as when using social media data. Our findings indicate that macro level dynamics of affective states of users of an online platform can be tracked with social media text, complementing surveys when self-reported data is not available or difficult to gather