Mohammad Reza Hassanpour Charmchi’s research while affiliated with University of Shahrood and other places

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Publications (3)


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Leveraging Bot-Connected User Accounts for Enhanced Twitter (X) Advertising Outcomes
  • Article
  • Full-text available

January 2024

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Journal of Artificial Intelligence Applications and Innovations

Mohammad Reza Hassanpour Charmchi

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With the expansion of social networks, their utilization in digital advertising has become a key factor in shaping public opinion and driving advertising campaigns —coordinated and targeted series of interactions between users on a specific topic. Properly directing these campaigns can focus many individuals on a particular subject, thereby creating effective campaigns. In this research, we introduce a method for developing campaigns suitable for digital advertising on Twitter (X). Users can leverage hashtags, tweets, comments, retweets, and other features of Twitter for a specified topic to build a campaign. This method engages known bot-connected user accounts on Twitter to interact with one another on a topic, generating initial attention and kickstarting the campaign. By then identifying influential users in that area and interacting with them, the campaign is further developed over time. To evaluate the performance of the proposed method, we considered two factors: the number of users involved in the campaign and the relevance of the selected content to the topic. We conducted this experiment with 50 bot-connected user accounts on Twitter. The results revealed that, through 116,594 interactions and receiving 246 responses from non-bot users, the proposed method was able to engage the audience within 5 days. These results demonstrate that our approach succeeded in attracting users and receiving feedback by publishing relevant content, suggesting its potential for real-world success.

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Citations (1)


... Table 7 summarizes all the metaheuristics and fitness functions related to code coverage testing using metaheuristics. The used meta-heuristics are the genetic algorithm (GA) (Fraser and Arcuri 2011;Charmchi and Cami 2021;Michael et al. 2001;Bottaci 2002;Sparks et al. 2007;Liu et al. 2008;Cao et al. 2009a, b;Rauf et al. 2010;Andrews et al. 2014;Shuai et al. 2013Shuai et al. , 2015aPałka et al. 2016;Paduraru et al. 2017;Arcuri 2017;Wei et al. 2018;Zhu et al. 2018;Wang et al. 2019b), evolutionary algorithm (EA) (Harman et al. 2002;Tlili et al. 2006;Baresel and Sthamer 2003;Afshan et al. 2013; LD(N(pa, AL(pa, i)), i)) Harman et al. (2002) Evolutionary algorithm Branch distance Bottaci (2002) Genetic algorithm Relational and logical predicate Baresel and Sthamer (2003) Evolutionary algorithm Node-node oriented fitness function Evolutionary algorithm The fitness of the sequence is determined based on the closest path to the test aim ...

Reference:

A systematic literature review on software security testing using metaheuristics
Paths-oriented Test Data Generation using Genetic Algorithm
  • Citing Conference Paper
  • December 2021