Project Details
- Name of your research project?
- Does this project receive funding from outside your academic institution? If yes, please list all your sources of funding.
- Describe your research project. Minimum 200 characters.
- What is your primary research question, hypothesis, or objective? What do you hope to learn?
- Describe how Twitter data via the Twitter API will be used in your research project. Minimum 200 characters.
- How and why will you use Twitter data in this project? What purpose does Twitter data serve as a datasource for your project? While this question may seem very similar to the prior question, we want to understand the role Twitter data plays in your project specifically. For example, are you using this data to study Twitter itself as a subject, or are you using this data as a method (or one of many methods) to study a different topic?
- Describe your methodology for analyzing Twitter data, Tweets, and/or Twitter users. Minimum 200 characters.
- In other words, what types of analyses do you intend to perform with Twitter data? This should be more descriptive of your tactics than the question above.
- Will your research present Twitter data individually or in aggregate?
- Think of it as presenting individual Tweets or users vs. aggregate statistics or models.
- Describe how you will share the outcomes of your research (include tools, data, and/or other resources you hope to build and share). Minimum 200 characters.
- We would like to know how and where you are interested in publishing or sharing your results. Will your analysis make Twitter content or derived information available to a government entity? If yes, list all government entities you intend to provide Twitter content or derived information to under this use case. Note that your own academic institution does not apply in this question. Minimum 200 characters.
# Example answers
Answers generated by ChatGPT :)
| Question | Answer |
|---|---|
| Project name | Homophily in an 2-Hop Neighborhood |
| Exernal funding | No |
# Describe your research project:
Our research aims to examine the phenomenon of homophily and the shadow profiles problem in a 2-hop neighborhood on Twitter using the Academic Twitter API. We aim to study the spread of influence in the context of Swiss referendums and how individuals with similar interests tend to cluster together on the platform. Our study will provide insight into the nature of online social networks and the ways in which information spreads through them, offering valuable information for future investigations of homophily and shadow profiles in online environments.
# What is your primary research question, hypothesis, or objective? What do you hope to learn?
The primary research question for this study would be: “How does homophily and the shadow profiles problem impact the spread of influence in a 2-hop neighborhood on Twitter in the context of Swiss referendums?”
The hypothesis for this study could be: “Individuals with similar interests tend to cluster together on Twitter and form echo chambers, leading to a reinforcement of their beliefs and biases, potentially affecting the spread of information and influencing the outcome of Swiss referendums.”
The objective of this study would be to gain a deeper understanding of the nature of online social networks, the ways in which information spreads through them, and how homophily and shadow profiles can impact this spread. The goal is to provide valuable information for future investigations of these phenomena in online environments.
# Describe how Twitter data via the Twitter API will be used in your research project.
The Twitter API will be utilized to collect data from the 2-hop neighborhood on Twitter in the context of Swiss referendums. This data will include information on user profiles, their tweets, and their interactions with others on the platform. The API will allow us to access a large and diverse dataset, enabling us to study the spread of influence and the impact of homophily and shadow profiles. The data collected from the API will be analyzed using various methods, including network analysis, natural language processing, and statistical analysis, to better understand how individuals with similar interests cluster together and how information spreads through their network. The findings from this study will help us to better understand the role of homophily and shadow profiles in shaping the spread of information on Twitter and the impact on the outcome of Swiss referendums.
# How and why will you use Twitter data in this project? What purpose does Twitter data serve as a datasource for your project?
Twitter data, accessed via the Twitter API, will be used in this project as a primary datasource to study the spread of influence in the context of Swiss referendums. The purpose of using Twitter data is to understand the nature of online social networks and how individuals with similar interests tend to cluster together on the platform, potentially affecting the spread of information and influencing the outcome of Swiss referendums. The data will be analyzed to investigate the impact of homophily and shadow profiles on the spread of influence in the 2-hop neighborhood on Twitter. The findings from this study will provide valuable insights into the ways in which information spreads through online social networks and the impact of homophily and shadow profiles on this spread. In this sense, Twitter data serves as a data source to study the spread of influence and homophily in online social networks, rather than studying Twitter itself as a subject.
# Describe your methodology for analyzing Twitter data, Tweets, and/or Twitter users.
The methodology for analyzing Twitter data, tweets, and users will likely involve the following steps:
- Data Collection: The Twitter API will be utilized to collect data from the 2-hop neighborhood on Twitter in the context of Swiss referendums. The data collected will include user profiles, tweets, and interactions between users on the platform.
- Data Cleaning and Preprocessing: The collected data will undergo cleaning and preprocessing to remove irrelevant information and format the data for analysis. This may include removing duplicates, correcting errors, and transforming the data into a usable format.
- Network Analysis: Network analysis will be used to examine the relationships between Twitter users in the 2-hop neighborhood. This will help to identify clusters of individuals with similar interests and the ways in which information spreads through their network.
- Natural Language Processing: Natural language processing techniques will be used to analyze the content of tweets and understand the sentiments and opinions expressed by users. This will help to identify the topics and issues being discussed in the context of Swiss referendums.
- Statistical Analysis: Statistical analysis will be used to quantify the relationships and patterns in the data. This may include regression analysis, hypothesis testing, and other techniques to investigate the impact of homophily and shadow profiles on the spread of influence in the 2-hop neighborhood.
These methods will be used to gain a deeper understanding of the nature of online social networks and the ways in which information spreads through them. The findings from this study will provide valuable insights into the impact of homophily and shadow profiles on the spread of information and the outcome of Swiss referendums.
# Will your research present Twitter data individually or in aggregate?
The research will present the Twitter data in aggregate. This means that the data collected from the Twitter API will be analyzed as a whole, rather than presenting individual data points. The goal is to understand the relationships and patterns in the data, such as the spread of influence and the impact of homophily and shadow profiles, rather than presenting the data for individual users or tweets. The aggregate data will be analyzed using network analysis, natural language processing, and statistical analysis to gain a deeper understanding of the nature of online social networks and the ways in which information spreads through them. The findings from this study will be presented in the form of charts, tables, graphs, and other visual representations to clearly communicate the results and insights gained from the analysis.
# Describe how you will share the outcomes of your research.
The outcomes of the research will be shared through a variety of channels, including academic publications, presentations at conferences, and online resources. The following are the ways in which the research outcomes will be shared:
Academic publications: The results of the research will be published in peer-reviewed journals to reach a wider audience of scholars and researchers in the field.
Conferences: The results will be presented at relevant conferences, providing an opportunity for the researchers to engage with other experts in the field and receive feedback on the findings.
Online resources: The research outcomes will be shared on websites and platforms, such as research blogs and open access repositories, to reach a broader audience and make the findings accessible to a wider public.
Data and Tools: Where possible, the data and tools used in the research will be shared and made publicly available, enabling other researchers to build upon the findings and further the understanding of the impact of homophily and shadow profiles on the spread of information in online social networks.
By sharing the outcomes of the research through these channels, the goal is to not only communicate the findings to a wider audience but also to encourage further research and collaboration in the field.
# We would like to know how and where you are interested in publishing or sharing your results.
Our research results will be published in peer-reviewed academic journals in order to reach a wider audience of scholars and researchers in the field. The goal is to disseminate our findings to the academic community and to encourage further research and collaboration in the field. We do not share any of the Twitter content or derived information with government entities, as we are committed to protecting the privacy of the individuals whose data we use in our research. We believe that it is important to follow ethical and legal considerations, such as privacy and data protection laws, in all aspects of our research, including the sharing of data and results. Our focus is on publishing our results in academic journals and sharing them with the academic community, and not with government entities.