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Working paper
Multi-Modal Networks Reveal Patterns of Operational Similarity of Terrorist Organizations
In: Terrorism and political violence, Band 35, Heft 5, S. 1065-1084
ISSN: 1556-1836
Learning future terrorist targets through temporal meta-graphs
In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence.
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The Power of Social Cognition
In: Journal of social structure: JoSS, Band 18, Heft 1, S. 1-23
ISSN: 1529-1227
Abstract
As human beings, we understand and make sense of the social world using social cognition. Social cognitions are cognitive processes through which we understand, process, and recall our interactions with others. Most agent-based models do not account for social cognition; rather, they either provide detailed models of task-related cognition or model many actors and focus on social processes. In general, the more cognitively realistic the models, the less they explain human social behavior and the more computationally expensive it is to model a single agent. In contrast, in this research an agent-based model containing an explicit model of social cognition is developed. Results from this model demonstrate that adding social cognition both improves the model veridicality and decreases computation costs.
Spectral Analysis of Social Networks to Identify Periodicity
In: The journal of mathematical sociology, Band 36, Heft 2, S. 80-96
ISSN: 1545-5874
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Working paper
Communication at a Distance: The Influence of Print on Sociocultural Organization and Change
In: Administrative Science Quarterly, Band 39, Heft 2, S. 336
Scientific Influence: An Analysis of the Main Path Structure in the Journal of Conflict Resolution
In: Knowledge, Band 14, Heft 4, S. 417-447
Researchers in scientific specialties and invisible colleges tend to cite each other in their written communications and especially in the Journals devoted to those specialities Such citations form a network through which there are many influence paths The main paths through such citation networks contain the key intellectual developments in these scientific fields If a specialty hangs together as a coherent field of research, one would expect that the citations within the field's journal should reflect the history ofthatfield and exhibit the degree of interconnectedness among the different researchers and their special subgroups Further, to the extent that a field is self-contained (i e , not borrowing on other fields for its key intellectual developments), the citation network within the journal should contain one or more main paths on which are located many of the key intellectual developments of the field These ideas are tested using citations in fifteen volumes of the Journal of Conflict Resolution published from 1957 to 1971
A social-event based approach to sentiment analysis of identities and behaviors in text
In: The journal of mathematical sociology, Band 40, Heft 3, S. 137-166
ISSN: 1545-5874
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Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions?
In: Online social networks and media: OSNEM, Band 28, S. 100198
ISSN: 2468-6964
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An exploratory analysis of COVID bot vs human disinformation dissemination stemming from the Disinformation Dozen on Telegram
In: Journal of computational social science, Band 7, Heft 1, S. 695-720
ISSN: 2432-2725
AbstractThe COVID-19 pandemic of 2021 led to a worldwide health crisis that was accompanied by an infodemic. A group of 12 social media personalities, dubbed the "Disinformation Dozen", were identified as key in spreading disinformation regarding the COVID-19 virus, treatments, and vaccines. This study focuses on the spread of disinformation propagated by this group on Telegram, a mobile messaging and social media platform. After segregating users into three groups—the Disinformation Dozen, bots, and humans, we perform an investigation with a dataset of Telegram messages from January to June 2023, comparatively analyzing temporal, topical, and network features. We observe that the Disinformation Dozen are highly involved in the initial dissemination of disinformation but are not the main drivers of the propagation of disinformation. Bot users are extremely active in conversation threads, while human users are active propagators of information, disseminating posts between Telegram channels through the forwarding mechanism.