Babak Anari

Mehrdad Ranjbar Khadivi, Shahin Akbarpour, Mohammad-Reza Feizi-Derakhshi et al.

With the widespread use of social networks, detecting the topics discussed on these platforms has become a significant challenge. Current approaches primarily rely on frequent pattern mining or semantic relations, often neglecting the structure of the language. Language structural methods aim to discover the relationships between words and how humans understand them. Therefore, this paper introduces a topic detection framework for social networks based on the concept of imitating the mental ability of word association. This framework employs the Human Word Association method and includes a specially designed extraction algorithm. The performance of this method is evaluated using the FA-CUP dataset, a benchmark in the field of topic detection. The results indicate that the proposed method significantly improves topic detection compared to other methods, as evidenced by Topic-recall and the keyword F1 measure. Additionally, to assess the applicability and generalizability of the proposed method, a dataset of Telegram posts in the Persian language is used. The results demonstrate that this method outperforms other topic detection methods.

Mehrdad Ranjbar-Khadivi, Shahin Akbarpour, Mohammad-Reza Feizi-Derakhshi et al.

In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods.

Mohammad Zeynali Azim, Saeid Alikhani, Babak Anari

This paper is about producing a new kind of the pairs which we call it MS-pairs. To produce these pairs, we use an algorithm for dividing a natural number $x$ by two for two arbitrary numbers and consider their related graphs. We present some applications of these pairs that show their interesting properties such as unpredictability, irreversible, aperiodicity and chaotic behavior.