Local Chapter Coventry, England Chapter
Coordinated by,
Status: Completed
Project Duration: 20 Feb 2023 - 27 Mar 2023
Open Source resources available from this project
Project background.
Effective communication and engagement with an audience are vital aspects of leadership. In recent years, social media platforms such as Twitter have become an essential tools for leaders to connect with their audience, shape public opinion and influence their followers. However, there is limited research on how the language use and audience engagement of male and female leaders on Twitter differs and how it affects their perceived leadership effectiveness.
Previous studies have shown that men and women use language differently. Men use more assertive and dominant language, while women use more cooperative and nurturing language. Additionally, research has also established that gender plays a role in how leaders are perceived. Women often face more barriers and biases in leadership positions, which can affect their effectiveness as leaders.
Given the significance of communication and engagement in leadership, it is essential to understand how language use and audience engagement on Twitter may impact the perceived leadership effectiveness of male and female leaders. The use of machine learning techniques can allow us to analyze large amounts of data and uncover patterns that may be otherwise difficult to detect. Therefore, this research study aims to use machine learning techniques to predict world leaders’ perception quotient (PQ) scores based on their tweets and examine the impact of gender on the PQ scores. By understanding the linguistic and engagement patterns of male and female leaders on Twitter, this research aims to provide insight into how language use and audience engagement may impact the perceived effectiveness of leaders and identify areas where gender bias may be present.
The problem.
The problem that the above research aims to solve is the lack of understanding of how language use and audience engagement on Twitter may impact the perceived leadership effectiveness of male and female leaders. Additionally, it also aims to identify areas where gender bias may be present in the perceived leadership effectiveness of male and female leaders. By using machine learning techniques to predict perception quotient (PQ) scores based on the tweets of world leaders and examining the impact of gender on these scores, the research aims to provide a deeper understanding of how language use and audience engagement may affect the perceived effectiveness of leaders. This knowledge can help leaders, organizations, and society to recognize and address any gender bias present in leadership and improve the effectiveness of leaders.
Project goals.
Project plan.
Week 1
Planning and proposal phase (1-2 weeks): The research question and objectives will be formulated, data sources will be identified, and a proposal outlining the research plan will be prepared.
Week 2
Data collection and preprocessing phase (1-2 weeks): Data will be collected from Twitter using APIs or web scraping tools and then cleaned and preprocessed to remove any irrelevant information. The data will also be analyzed to ensure it is suitable for the research.
Week 3
Feature extraction and model building phase (1-2 weeks): In this phase, relevant features will be extracted from the data, such as sentiment analysis, sentiment, and topic modeling. The data will then be split into training and testing sets, and machine learning models will be built and trained on the data
Week 4
Perception Quotient (PQ) score calculation phase (1-2 weeks): In this phase, the PQ score will be calculated by using a combination of metrics such as sentiment analysis, audience engagement, and language use. The PQ scores will be calculated for both male and female leaders.
Week 5
Model evaluation and analysis phase (2-4 days): In this phase, the performance of the models will be evaluated using metrics such as accuracy, precision, and recall. The results will be analyzed to identify patterns and trends, and to draw meaningful conclusions.
Week 6
Report and presentation phase (1-2 weeks): In this phase, the findings will be presented in a report and a presentation. The report will include an introduction, literature review, methods, results, discussion, and conclusions. The presentation will be used to present the findings to a wider audience.
Week 7
Conclusion and Final Submission (1 week): In this phase, the final report and the presentations will be submitted along with the code, data, and other materials. The project will be closed and the team will be debriefed.
Learning outcomes.
The skills and experiences the participants will take out from this project include:
Technical Skills: Participants will gain hands-on experience with machine learning techniques and tools, such as machine learning, natural language processing (NLP), data preprocessing, and feature extraction. Additionally, participants will also learn how to use Python and its various libraries to analyze and visualize data.
Analytical Skills: Participants will learn how to critically analyze and interpret large amounts of data, identify patterns and trends, and draw meaningful conclusions.
Research Skills: Participants will learn how to design, implement and report a research project. This includes understanding research ethics and the scientific method, formulating research questions, and selecting appropriate data collection and analysis methods.
Communication Skills: Participants will learn how to effectively communicate their findings and recommendations to both technical and non-technical audiences.
Project management skills: Participants will learn how to manage time, resources, and stakeholders effectively to complete a project on time and within budget Understanding of leadership, gender, and communication: Participants will gain a deeper understanding of the influence of gender on the communication and engagement of world leaders on social media platforms like Twitter.
Experience working with large datasets: Participants will learn how to work with large datasets from social media platforms, and gain experience in data cleaning, preprocessing, and analysis.
Teamwork and collaboration: Participants will learn how to work effectively in a team environment, share ideas and collaborate to solve problems.
