ترغب بنشر مسار تعليمي؟ اضغط هنا

Inferring Social Rank in an Old Assyrian Trade Network

103   0   0.0 ( 0 )
 نشر من قبل David Bamman
 تاريخ النشر 2013
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

We present work in jointly inferring the unique individuals as well as their social rank within a collection of letters from an Old Assyrian trade colony in Kultepe, Turkey, settled by merchants from the ancient city of Assur for approximately 200 years between 1950-1750 BCE, the height of the Middle Bronze Age. Using a probabilistic latent-variable model, we leverage pairwise social differences between names in cuneiform tablets to infer a single underlying social order that best explains the data we observe. Evaluating our output with published judgments by domain experts suggests that our method may be used for building informed hypotheses that are driven by data, and that may offer promising avenues for directed research by Assyriologists.



قيم البحث

اقرأ أيضاً

This paper presents the first data-driven analysis of Gettr, a new social network platform launched by former US President Donald Trumps team. Among other things, we find that users on the platform heavily discuss politics, with a focus on the Trump campaign in the US and Bolsonaros in Brazil. Activity on the platform has steadily been decreasing since its launch, although a core of verified users and early adopters kept posting and become central to it. Finally, although toxicity has been increasing over time, the average level of toxicity is still lower than the one recently observed on other fringe social networks like Gab and 4chan. Overall, we provide a first quantitative look at this new community, observing a lack of organic engagement and activity.
The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majori ty of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than $800$ university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individuals characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?
How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the death of distance, physical proximity is still a major factor in social tie formation and maintenance in vi rtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resembling to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.
It is generally accepted that neighboring nodes in financial networks are negatively assorted with respect to the correlation between their degrees. This feature would play an important damping role in the market during downturns (periods of distress ) since this connectivity pattern between firms lowers the chances of auto-amplifying (the propagation of) distress. In this paper we explore a trade-network of industrial firms where the nodes are suppliers or buyers, and the links are those invoices that the suppliers send out to their buyers and then go on to present to their bank for discounting. The network was collected by a large Italian bank in 2007, from their intermediation of the sales on credit made by their clients. The network also shows dissortative behavior as seen in other studies on financial networks. However, when looking at the credit rating of the firms, an important attribute internal to each node, we find that firms that trade with one another share overwhelming similarity. We know that much data is missing from our data set. However, we can quantify the amount of missing data using information exposure, a variable that connects social structure and behavior. This variable is a ratio of the sales invoices that a supplier presents to their bank over their total sales. Results reveal a non-trivial and robust relationship between the information exposure and credit rating of a firm, indicating the influence of the neighbors on a firms rating. This methodology provides a new insight into how to reconstruct a network suffering from incomplete information.
Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have rep eatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive social media virtual companion for educating and supporting the entire students community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا