SoSLab

Complex systems research laboratory.

SosLab focuses on Complex Systems, such as social network analysis, migration, fame, information spread, and big data issues. One of our major focuses is on exploring microblogging systems. Recent studies include identifying human readable and machine interpretable topics in microblogs, and organized behavior detection. The relationships among microblogger contributions and other media is also a line of inquire. Another line of inquiry is detecting bots and trust aspects of users as well as content.

  • A patent to our study: Content sensitive document ranking method by analyzing the citation context

  • Gossip on Weighted Networks

  • S-BounTI: Extracting semantic topics from microblog posts

  • Parent oriented teacher selection causes language diversity

  • Context sensitive article ranking with citation context analysis

  • Iterated Prisoners Dilemma with limited attention

  • Attention Competition with Advertisement

  • Boun-TI: Extracting Topics From Twitter And Representing Them Using Wikipedia Page Titles

Parent oriented teacher selection causes language diversity

An evolutionary model for emergence of diversity in language is developed. We investigated the effects of two real life observations, namely, people prefer people that they communicate with well, and people interact with people that are physically close to each other. Clearly these groups are relatively small compared to the entire population. We restrict selection of the teachers from such small groups, called imitation sets, around parents.

Gossip on Weighted Networks

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network.

S-BounTI

S-BounTI is a topic identification approach that identifies topics of a crowd of microblog users. It represents topics using Topico ontology which is designed to express microblog topics. S-BounTI and Topico are products of Ahmet Yildirim's PhD work under the supervision of Suzan Uskudarli, members of SosLab (Complex Systems Laboratory) in Department of Computer Engineering, Bogazici University, Istanbul, Turkey.

Context sensitive article ranking with citation context analysis

In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network.

The Dose of the Threat Makes the Resistance for Cooperation

Greater memory size is unfavorable to evolutionary success when there is no threat. In contrast, the presence of an appropriate level of threat triggers the emergence of a self-protection mechanism for cooperation.

Accelerometer Based Calculator For Visually-Impaired People Using Mobile Devices

This study aims to find an alternative approach to classify 20 different gestures captured by iPhone 3GS’s built-in accelerometer and make high accuracy on user-independent classifications. The method is based on Dynamic Time Warping (DTW) with dynamic warping window sizes. The first experimental result, which is obtained from collected data set, gives 96.7% accuracy rate among 20 gestures with 1062 gesture data totally.

Boun-TI: Extracting Topics From Twitter And Representing Them Using Wikipedia Page Titles

In this project, we have researched if Wikipedia page titles can be used to represent topics that are talked about in Twitter and proposed an approach to do that. In contrast to existing topic extraction methods that extracts topics from only one tweet, our approach extracts topics from multiple posts but express them using words, our approach expresses topics using title of Wikipedia pages.

Iterated Prisoners Dilemma with limited attention

In order to beat defection, players do not need a full memorization of each action of all opponents. There exists a critical memory capacity threshold to beat defectors.