Conference Agenda

This study examines the evolution of socio-economic relationships in response to the algorithmic transformations of digital platforms, with a particular focus on TikTok and ChatGPT. The objective is to analyze the extent to which the algorithmic structures of these platforms influence user interactions, access to information, and consumption behaviors. Building upon established theoretical frameworks, it has been observed that the transition from traditional social networks to the algorithmic selves characteristic of TikTok (Bhandari & Bimo, 2022; Boccia Artieri & Donato, 2024) has evolved into a more individualized and mechanized dynamic in platforms such as ChatGPT (Gutiérrez, 2023), ultimately materializing the concept of self-datafication (Couldry, 2020). Within this framework, the study critically investigates how these socio-political transformations have redefined economic structures, not only shaping new consumption practices but also constraining the agency and relational dynamics of traditional economic actors.

The research is structured into two methodological phases. The first phase entails a platform ethnography of TikTok and ChatGPT, aimed at examining their technical and structural configurations as well as the social and cultural practices they foster. The second phase consists of a more extensive empirical investigation, conducted through the administration of semi-structured questionnaires to a sample of 110 Italian participants, to assess the impact of algorithmic mediation on consumer behavior.

The anticipated findings seek to illustrate that the technological transformations of digital platforms are not merely advancements in computational design but manifestations of broader political and cultural shifts with profound implications for economic dynamics, decision-making processes, and spending patterns.

The aim of my speech is to reflect, from a theoretical-critical perspective, on the ideological elements that support the content creator economy. The reciprocity of influence between the actions of online creators and those of online users could be considered a revival of “catallactic competition”, first theorized in the 1940s by economist and ante-litteram promoter of neoliberalism Ludwig von Mises. A fundament of Mises’ economic model was a new conception of freedom, according to which no external interference must prevent individuals from expressing themselves within the wider market. Today, this freedom to act in one’s own interest within the market is actuated by the interplay of various individuals involved in the platform economy, in which consumers are no longer considered passive, but “sovereign”. They influence the choices of content creators who, in turn, are able to entrepreneurially react by shaping their influential content to meet more people’s needs. This reaction, however, is but an illusion of exercising catallactic freedom. In such a socio-economic model, the individual seems freed from external imposition of preordained market logic yet is not at all detached from it, because herein freedom is not opposed to constraint but is based on constraint. Within the tight and immediate loop of content creation and content consumption it can become impossible to imagine realities other than those efficiently offered by the market and its enabling technologies. In this order, everyone becomes a potential entrepreneur but also an unconscious reproducer: the individualistic ideology of self-affirmation enslaves people to the marketable self.

This research proposes a hybrid analytical framework for examining how design and content attributes shape discussion network structures. The framework integrates quantitative and qualitative content analysis, social network analysis, and path modeling, providing a structured approach to uncovering how these attributes interact and influence discussion dynamics.

The framework employs path analysis using Hayes’ (2022) PROCESS macros, enabling a detailed examination of mediation effects. Within this analysis, we assign attributes as mediators, while network measures serve as the dependent variables. This approach allows us to assess the existence of different causal paths and, together, illustrate an underlying mechanism.

The framework is demonstrated through a case study of discussion networks derived from online news comments. These networks function as deliberative spaces and are structured as reply networks, where a directed and weighted tie is formed whenever a commenter responds to another comment, linking the responding commenter-node to the top commenter-node. Each news story generates a distinct discussion network.

In this study, network structures are calculated for each story and incorporated into a model alongside its measured attributes. The model also includes control variables such as news story length, topic, and publishing year to account for potential confounding factors. This approach allows us to trace the path between online environment design, content features, and discussion structure The analysis results provide new findings that address a long-standing knowledge gap regarding audience engagement with data journalism, propose underlying causal mechanisms, and contribute empirical insights into deliberation theory.

Power is everywhere: social reality is based on power relationship whether formal or informal. A city does not have a power per se but concentrates people with power relations. Despite numerous studies of city power, there is a research gap on multilevel city power, and on the link between the power of individuals that transforms into power of cities in global economic networks.

We can wonder how micro-level processes such as human interactions shape the power of cities on global scale? This concerns a question of trust and power in social networks (Castells, 2013), in particular, the networks of those who take key decisions in cities, but also those who take decisions concerning places of the world outside the city (like headquarters). Are these two kinds of networks linked to each other like it is suggested in “Local buzz, global pipelines” (Bathelt et al., 2004)? These networks of decision makers create power asymmetries in cities, control urban governance structures and globalize cities from top-down and bottom-up, thus reinforcing the influence of a city on a world scale.

We conduct a literature review based on the topic modeling method, to underline the main associations between different scales and concepts. Using Google Scholar, we construct several corpuses of literature. Analyzing the networks of terms and highlighting words’ associations allows us to determine the common points and differences between diverse power discourses, and to identify the key elements one can use to define a multilevel/multiscale city power.

References:

Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in human geography, 28(1), 31-56.

Castells, M. (2013). Communication power. Oxford University Press, USA.

During periods of threat, such as with the COVID-19 pandemic, the public looks to officials to understand the situation, its causes, and its ramifications. Among the communication strategies employed by public entities is the use of causal narratives, a highly condensed, structured method of imparting information that describes events in terms of the conjunction of a cause and an effect. As narratives with particular structures have the potential to relay essential, clarifying, and possibly life-saving information to a lay audience, understanding narrative structure is important for developing an evidence base to inform future crisis response. However, an individual’s conception of a situation is rarely informed by a single speakers’ narratives but rather by a welter of messages from different agencies that may be imperfectly remembered and lumped together into an aggregate narrative. Thus, there is a need to examine the broader discourse constructed by articulated causal narratives by responding agencies to the public.

In this study, we reconstruct the semantic network of causal narratives utilized by organizations in public communications during the COVID-19 pandemic, characterizing the underlying structure of the discourse, and assessing the degree to which officials en masse provided clear, consistent, and actionable information on the evolving crisis. A key feature of our work is our evaluation of large language models (LLMs) for extracting complex semantic network features from massive corpuses of heterogeneous text. In addition to comparing the efficacy of LLMs to human performance, we also experiment with both novel and known methods for improving LLM accuracy.

We present a methodology to analyze semantic (co-occurrence) networks of academic papers in a given subfield with complementary and different entropy-based measures to obtain a rigorous and quantitative assessment of unique language usage at concept-level and text-level.

After cleaning each text through tokenization, stop-word removal, and lemmatization, and by following the semantic network creation procedure, we create an adjacency matrix representing co-occurrences between words appearing in the same sentence, conducted by AutoMap (Carley et al., 2013) and ORA (Carley et al., 2014). Each word will then have an associated probability distribution over its neighbors, allowing us to compute its Shannon entropy H(w) and reinterpret it as a quantitative reserve for entropy-based calculations.

We focus on three entropy-based measures. After the implementation of respective normalization steps at each measure, first, each node w has a word-level entropy H(w) reflecting the unpredictability of its immediate neighbors. Second, a local excess entropy Elocal(w) subtracts H(w) from log2(∣N(w)∣), where ∣N(w)∣ is the number of neighbors for node w, thus comparing the observed entropy to a uniform distribution among those neighbors. Third, a global excess entropy Eglobal(w) subtracts H(w) from log2(∣V∣), taking the entire vocabulary into account for its maximum entropy baseline. Finally, by averaging these word-based excess entropies across all nodes, we obtain a single text-level aggregate measure of structural predictability for each text. These complementary entropy-guided quantities provide concept-based and text-based systematic comparisons of linguistic and conceptual structuredness, uniqueness, and comparative basis for texts and concepts that characterize a given academic subfield.

Global Citizenship Education (GCED) is an expanding and internationally recognized educational framework. This paper presents a novel approach to analyzing the GCED policy landscape, drawing on political science research that examines how innovative responses to global challenges emerge through the actions and discourse of organizations involved in policymaking. The study is based on 51 interviews with key GCED advocates in Europe and North America and employs Social Network Analysis and Content Analysis to explore both the structural dynamics of policy networks and the diverse conceptualizations of GCED among participating organizations.

Findings reveal that organizations at the center of policy networks often adopt multiple interpretations of GCED in their discourse. This conceptual fluidity has both advantages and challenges. On one hand, it enhances adaptability across various educational settings; on the other, the absence of a unified definition complicates implementation. Additionally, there is a risk that GCED could be absorbed into mainstream education policies in ways that dilute its transformative intent.

The core-periphery structure of the policy network has three main implications. First, the absence of cohesive subgroups or dominant actors (Wasserman & Faust, 1994) suggests a decentralized network where influence is more evenly distributed, fostering frequent interactions among a diverse range of stakeholders. Second, while core actors are embedded in dense, closely bonded networks, these structures may inhibit innovation (Burt, 1992), making them less likely to endorse radical GCED positions. Third, the lack of clear political divisions underscores the need to examine policy beliefs (Sabatier, 1988), particularly given that GCED spans a spectrum from soft to critical approaches (Andreotti, 2014).

Furthermore, the “multivocality” of core actors presents both opportunities and risks. Their ability to navigate multiple policy arenas and integrate GCED into diverse political agendas strengthens advocacy efforts. However, this flexibility also raises concerns that softer, more neutral, and less critical interpretations of GCED could become dominant, limiting its potential for transformative change. Balancing inclusivity with maintaining GCED’s critical edge remains a pressing challenge for stakeholders in the field.

Semantic networks (SemNets) derived from relatedness judgment tasks (RJTs) are thought to reflect semantic memory organization. Participants typically judge the relation between written words. Interestingly, because processing written words requires accessing lexical information, it is unclear whether these networks capture purely conceptual (i.e., amodal semantic) or lexico-semantic (i.e., word-based) structures. Stimuli in different modalities share common semantic representations, but differ significantly in processing demands and the neural pathways leading to semantic activation. This study examines whether RJT-derived SemNets reflect conceptual or lexico-semantic information, and whether these effects are modality specific.

One hundred and two native English speakers completed three modality-specific versions of the RJT, with written words, spoken words, and images. In each version, items referred to the same 28 concepts. Participants provided pairwise relatedness ratings, from which SemNets were generated. Global network metrics (i.e., diameter, average shortest path length, clustering coefficient, modularity, small-worldness) were compared across modalities using generalized linear mixed models, alongside analyses of local metrics and ratings distributions.

No significant differences were found between written- and image-derived networks, suggesting that lexical processes utilized in the written RJT do not influence the networks. A marginal trend toward more condensed, small-world networks in the spoken version was observed, potentially due to differing processing demands in the spoken version and modality-dependent methodological differences. Semantic similarity (fastText-derived cosine distance) was the strongest predictor of ratings across all versions.

We cannot rule out that RJT-derived semantic networks accurately reflect conceptual structures, reinforcing the validity of RJTs to assess semantic memory.

Citation analysis is commonly found in studies of scientific research production but also fits sociological legal studies. Detailed documentation of decisions enhances the judiciary's collective claim to legitimate authority.

District Courts are of interest since they belong to the bottom of the organizational hierarchy (under circuit courts of appeal and the Supreme Court). We hypothesize that since they possess lower claims to hierarchical authority, they might resort to documentation to enhance it. The hypothesis could find support in a dense citational network. Previous studies on appeals courts indicate a structure that is dense at the center with sparse edges for most nodes, while Supreme Court decisions are overloaded with citations.

To test this hypothesis, we collected all termination documents from 1924 to 2024 from the online JUSTIA US Law legal database. Documents are processed into machine-readable text using Python's pypdf module. Text analysis (LexNLP, Spacy, and custom rules-based models) is also applied to extract author and legal citations. This results in a network modeled either as (1) document-to-document, (2) judge-to-document (bipartite), or (3) judge-to-judge (projected).

Preliminary results of the projected judge-to-judge network show a structure of unconnected or loosely connected nodes and structural holes, i.e. overall unconnectedness. Our ongoing analyses describe a stratified network system within a social system in which network connections are sparse at the bottom stratum (district courts), much denser at the middle stratum (appeals courts), and exceedingly dense at the Supreme Court level.

Foundation.

We build on "A network visualisation method of cognitive dissonance in discourses: discovery, validity, application, and extensions", which was shared at Sunbelt 2017. Specifically, Meta-Ethical Stances (MESs) were then determined by expert interviews of Comparative Philosophy of Ethics academics and are leveraged here in a novel fashion .

Method.

Our research is conducted in 5 stages :

- stage I is to prompt four Large Language Models (LLMs) to semiotically detect the presence or not of each aforementioned MESs in all sentences present in a panel of diverse organizations' English-language annual reports from years 2021 to 2023 [diversity here includes geopolitic, industry vertical, legal status].

- stage II is to statistically manually qualify, for each MES, 3 sub-samples of sentences (all LLMs detect this MES, some do and some don't, and none do); this manual check is undertaken by the team with second line support from Philosophy academics expert in each MES).

- stage III is to retain only the best LLMs, as qualified in stage II, for stage IV onwards (when the socio-semiotic network analysis kicks off).

We now have annual reports of select organizations (public, for profit, non profit, public-private partnerships, state-owned enterprises,…) parsed to derive the implicit or explicit expression in any passage of an MES-laden statement (if any) and the stance it is derived from .

Consolidation of these individual, weighed indications of expressed MESs creates a two-mode network map where nodes are either organizations or select organizational categories, and stances and vertices either are "organization, or region, or culture, or vertical industry, or entity of legal type (i.e locutor or locutor aggregate set) Expresses stance" or "stance Contradicts stance".

- stage IV to analyse these networks from the perspective of locutor aggregate hypergraphic centrality, i.e treating the vertices as nodes and visually explore visual representations of regions, cultures, vertical industries, legal types of entities and the stance mix and breadth of their set members (even perhaps longitudinally)

- stage V is to, in parallel, ponder future research avenues, and speculate on implications from applying our novel approach to discourse analysis for stakeholders (including those sensitive to sustainability and governance issues).

As urbanization becomes more generalized in the world, the quest for healthy and sustainable cities is met with diverse urban risks and challenges. To address these challenges, researchers have increasingly turned to interdisciplinary approaches that integrate diverse methods and perspectives (Quah, 2016). Nevertheless, the pursuit of long-term and comprehensive urban health intervention demands a more intricated understanding of the interdisciplinary synergies on specific urban health topics (Rutter et al., 2017, Gatzweiler et al., 2021).

To address this gap, our research leverages text mining techniques, topic modeling, and network analysis (Van Eck and Waltman 2010, Aria and Cuccurullo, 2017) on a comprehensive corpus of urban health literature sourced from databases such as Web of Science, PubMed, and Scopus. This topic modelling illuminates both the overlapping themes and unique nuances across diverse scientific domains by revealing how boundary objects—such as keywords, topics, and indicators in these research—act as anchors for multidisciplinary collaboration. The visualizations generated through network analysis further demonstrate how distinct fields—from public health and environmental science to urban planning—converge to tackle shared urban health challenges between different scientific disciplines.

This fine-grained analysis of word networks not only delineates the conceptual boundaries within urban health research but also enriches our understanding of how ideas diffuse across multidisciplinary fields. Ultimately, this method contributes to the integrated knowledge base, informing innovative strategies for urban health interventions through the lens of language and network dynamics.

This study was conducted at the University of Kassel (Germany) as part of the PRONET project (Professionalisation through Networking), funded by the BMBF. The central aim of PRONET was to foster active collaboration among the participating 34 sub-projects, bringing them together to jointly develop new outputs such as concepts, materials, seminars, and workshops. These efforts were designed to advance research, teaching, and practice in teacher education.

The evaluation of collaboration between the sub-projects was conducted through an online survey at three points in time (winter 2015, summer 2017, and winter 2018). The resulting connections between the sub-projects were analyzed using network analysis. The goal of the evaluation was to assess the impact of cooperation on the activities of the sub-projects.

This study seeks to operationalize cognitive proximity between projects in teacher education and to examine its influence on collaboration. Cognitive proximity was operationalized by analyzing the frequency of word usage (correspondence analysis) in the publications of the projects. In this context, the use of different words increases the distance between two projects, while the use of similar words brings them closer together.

The first research question addressed in this study is whether cognitive proximity directly influences network collaboration. This question was explored by testing the ego, alter, and similarity effects of cognitive proximity on collaboration between projects.

The second research question examines whether cognitive proximity influences the reciprocity of collaboration (mutuality) and the clustering within the collaboration network (triadic closure). The longitudinal analysis was conducted using a stochastic actor-oriented model (SOAM). A key finding related to the first question was that the alter effect of cognitive proximity has a negative impact on collaboration. In other words, as expected, projects that are cognitively distant are less likely to be mentioned or chosen as collaboration partners.

When concepts spread over a network of organizations, they are adapted and translated. In an organization field, organizations adopt elements of concepts that help them reflect environmental expectations they face, they leave out other elements that they do not deem to be relevant or a good fit in their context, they creatively recombine them with other concepts that are already at place in their organizations, potentially creating new conceptual ideas. As a result, there exists a variety of instances of concepts, even within one organizational field. But how can we measure and make sense of this variety of concepts’ meanings within and across fields? And how can this variety of concepts be explained by the network structure of the field?

We argue that the idea of translation is valuable for addressing this question. Concepts travel within and across organizational fields through networks and cultural linkages. In the network, the members are acting as both recipients and transmitters of concepts which, in turn, allows for an alteration or recombination of those. The greater the diversity of receiving concepts, the greater the recombinatorial possibilities. Organizational fields are characterized by network structures and a shared meaning system driven by isomorphic pressures. Field constellations can thus help making sense of the variety of concepts observable among field members.

We explore the variety of concepts in the context of three fields: The fields evolving around Blockchain technology, the issues of cooperativism and sharing economy. We argue that these three fields are promising for exploring concept variety as they all are based on conceptual ideas of decentralization and sharing: Blockchain technology allows decentralized data storage. Cooperativism involves shared ownership and decentralized decision-making in organizations. The sharing economy is based on the idea of organizing shared access to distributed products, services and resources. Based on the argument that textual embedding models are a powerful approach to representing multidimensional conceptual spaces, we apply these models to the texts we find on organizational websites using a measurement approach. Different from Acevas & Evans (2023), we will use sentence embeddings such as SBERT (Reimers & Gurevych, 2019) where sentences (or paragraphs) are already the unit of text the model was trained on. We use Wikipedia as data source to identify and describe central dimensions for the three conceptual ideas characterizing the cores of the three fields:

Blockchain technology, cooperative organizational form and sharing economy. Wikipedia reflects the social stock of knowledge as conceptualized in the theorization literature.

Our contributions are twofold. Theoretically, we contribute by emphasizing translation as an agentic process where the adoption of elements of a concept depends on both the position of an organization within the conceptual space and the social space.

Methodologically, we contribute by utilizing language models to measure the variety of the three concepts that are reflected on the websites. This allows us to trace which elements are adopted and how they are recombined with the elements of the other concepts.

This paper explores how fluctuations in the importance of brands within online news can impact retail store sales across different product categories. Using a dataset of over 250,000 news articles from The New York Times between 2019 and 2023, along with daily sales data from more than 60,000 U.S. stores, we explore the connection between brand importance and consumer behavior in physical retail environments. We analyze semantic networks and employ the Semantic Brand Score (SBS) to evaluate the prevalence, diversity, and connectivity of well-known brands across sectors such as grocery, fast food, and specialty merchandise. Our findings demonstrate that a one standard deviation increase in brand importance is associated with a sales increase of over 1%, with this effect persisting for up to five weeks. In contrast to the short-lived impact of social media, the influence of news media on sales appears to be more sustained. This paper contributes to the literature by shifting focus from social media to online news, offering detailed insights into the media’s impact across a broad range of retail sectors, and using big data in place of traditional surveys. Our results highlight the importance of maintaining a strategic presence in news media and suggest that retailers can benefit from continued media coverage, which has the potential to shape consumer behavior over an extended period.