Raji Ghawi
Biographic Data
| ID | 906963 |
|---|---|
| NAME | Raji Ghawi |
| GIVEN NAMES | Raji |
| FAMILY NAME | Ghawi |
| SIGNATURE | GHAWI R |
| AFFILIATIONS | Munich School of Philosophy |
| ORCID | 0000-0002-2865-2014 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Predicting member churn in dynamic groups
Predicting churn in dynamic groups remains a challenge in the field of social network analysis and group behavior modeling. While previous research has focused predominantly on static networks or individual-level predictors, the complex interplay of group dynamics over time is still underexplored. This work proposes a multidimensional framework for analyzing and predicting member churn in dynamic groups by integrating structural and temporal dime…
Online misogyny against female candidates in the 2022 Brazilian elections
Technology-facilitated gender-based violence has become a global threat to women’s political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyze 10 million Tweets directed at female candidates in the Brazilian election in 2022 and examine their reactions to online misogyny. Using a self-trained machine learning classifier to detect Portuguese misogynistic Tweets and a quantitative analysis…
Identifying stages in the lifespan of dynamic groups
Identifying lexical change in negative word-of-mouth on social media
Negative word-of-mouth is a strong consumer and user response to dissatisfaction. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. In this work, we examine the change of vocabulary to explore the outbreak of online firestorms on Twitter. The sudden change of an emotional state can be captured in language. It reveals how people conn…
A community matching based approach to measuring layer similarity in multilayer networks
Diffusion dynamics of influence in a social network of intellectuals
The history of intellectuals consists of a complex web of influences and interconnections of philosophers, scientists, writers, their work, and ideas. To understand how did these influences evolve over time, we mined a network of influence of over 12,500 intellectuals, enriched it with a temporal dimension dividing the history into six eras. We analyze time-sliced projections of the network into within-era, inter-era, and accumulated-era networks…
Improving team performance prediction in MMOGs with temporal communication networks
Virtual teams are becoming increasingly important. Since they are digital in nature, their "trace data" enable a broad set of new research opportunities. Online Games are especially useful for studying social behavior patterns of collaborative teams. In our study, we used longitudinal data from the massively multiplayer online game Travian collected over a 12-month period that included 4753 teams with 18,056 individuals and their communication ne…
Diffusion dynamics of influence in a social network of intellectuals
The history of intellectuals consists of a complex web of influences and interconnections of philosophers, scientists, writers, their work, and ideas. To understand how did these influences evolve over time, we mined a network of influence of over 12,500 intellectuals, enriched it with a temporal dimension dividing the history into six eras. We analyze time-sliced projections of the network into within-era, inter-era, and accumulated-era networks…
Improving team performance prediction in MMOGs with temporal communication networks
Virtual teams are becoming increasingly important. Since they are digital in nature, their "trace data" enable a broad set of new research opportunities. Online Games are especially useful for studying social behavior patterns of collaborative teams. In our study, we used longitudinal data from the massively multiplayer online game Travian collected over a 12-month period that included 4753 teams with 18,056 individuals and their communication ne…
Identifying lexical change in negative word-of-mouth on social media
Negative word-of-mouth is a strong consumer and user response to dissatisfaction. Moral outrages can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. In this work, we examine the change of vocabulary to explore the outbreak of online firestorms on Twitter. The sudden change of an emotional state can be captured in language. It reveals how people conn…
A community matching based approach to measuring layer similarity in multilayer networks
Identifying stages in the lifespan of dynamic groups
Predicting member churn in dynamic groups
Predicting churn in dynamic groups remains a challenge in the field of social network analysis and group behavior modeling. While previous research has focused predominantly on static networks or individual-level predictors, the complex interplay of group dynamics over time is still underexplored. This work proposes a multidimensional framework for analyzing and predicting member churn in dynamic groups by integrating structural and temporal dime…
Online misogyny against female candidates in the 2022 Brazilian elections
Technology-facilitated gender-based violence has become a global threat to women’s political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyze 10 million Tweets directed at female candidates in the Brazilian election in 2022 and examine their reactions to online misogyny. Using a self-trained machine learning classifier to detect Portuguese misogynistic Tweets and a quantitative analysis…
Computer Science (5 works) · Complex Network Analysis Techniques (3 works) · Artificial Intelligence (2 works) · Customer churn and segmentation (2 works) · Mathematics (2 works) · Opinion Dynamics and Social Influence (2 works) · Political science (2 works) · Psychology (2 works) · Social media (2 works) · Sociology (2 works)