Roadmap

This page is a draft and is subject to change

Monthly Plan

Phase	Task Description	Start Date	End Date	Duration
Planning & Literature Review	Define research questions, project scope, ethical considerations, and review foundational literature	10 Feb 2025	10 Mar 2025	1 month
Data Collection Setup	Configure APIs/scrapers, set up infrastructure, and run pilot tests	11 Mar 2025	30 Apr 2025	~1.5 months
Data Collection	Continuously gather data from the chosen platforms	1 May 2025	30 Sep 2025	5 months
Data Processing & Cleaning	Preprocess, clean, and validate incoming data	1 Jun 2025	30 Sep 2025	4 months
Analysis & Interpretation	Conduct analyses	1 Oct 2025	15 Nov 2025	1.5 months
Reporting & Presentation	Compile findings into a final report and prepare for presentation	16 Nov 2025	20 Dec 2025	~1 month
Buffer & Revisions	Final edits, incorporate feedback, and complete any outstanding work	21 Dec 2025	1 Jan 2026	~10 days

Weekly Plan

Week	Date Range	Tasks
1	Feb 10 – Feb 16	Project kickoff, meetings
2	Feb 17 – Feb 23	Initial literature research, identifying key digital humanities sources.
3	Feb 24 – Mar 2	Expanding literature review and drafting preliminary methodology.
4	Mar 3 – Mar 9	Finalizing research questions and considerations, completing research design documentation.
5	Mar 10 – Mar 16	Data collection setup, investigating APIs/scraping tools.
6	Mar 17 – Mar 23	Setting up development environment and data collection tools.
7	Mar 24 – Mar 30	Developing and testing initial data collection scripts, verifying API connectivity etc.
8	Mar 31 – Apr 6	Refine scripts; troubleshoot API issues; set up data storage infrastructure (e.g., databases, backups).
9	Apr 7 – Apr 13	Establishing db infrastructure, discussing backup strategies.
10	Apr 14 – Apr 20	Pilot data collection, assessing data quality and completeness, discussing improvements.
11	Apr 21 – Apr 27	Analyzing pilot results, adjusting collection parameters, and integrating multiple platforms if needed.
12	Apr 28 – May 4	Finalizing data collection setup across platforms, performing full-scale collection.
13	May 5 – May 11	Full-scale data collection and monitoring of initial data flows.
14	May 12 – May 18	Continueing data collection, adjusting queries/parameters as needed.
15	May 19 – May 25	Establishing a baseline dataset, documenting performance metrics and any issues encountered.
16	May 26 – Jun 1	Monitoring and maintaining data collection processes; initiating preliminary cleaning routines.
17	Jun 2 – Jun 8	Starting structured data cleaning, handling duplicates.
18	Jun 9 – Jun 15	Refining cleaning pipelines, and handling missing data.
19	Jun 16 – Jun 22	Expanding data queries (e.g., additional hashtags/topics); monitor collection scale and quality.
20	Jun 23 – Jun 29	Mid-cycle review: Assessing data quality, methods, and collection process.
21	Jun 30 – Jul 6	Updating pipeline; optimizing data collection scripts based on mid-cycle feedback.
22	Jul 7 – Jul 13	Ongoing data collection: Monitoring API rate limits
23	Jul 14 – Jul 20	Quality checks, starting exploratory analysis
24	Jul 21 – Jul 27	Continueing data collection, documenting trends, and updating cleaning routines as necessary.
25	Jul 28 – Aug 3	Consolidating the dataset; performing backups and evaluating overall dataset completeness.
26	Aug 4 – Aug 10	Addresssing any data gaps; updating collection parameters; refining automation and error handling.
27	Aug 11 – Aug 17	Maintaining continuous data collection; monitoring for API updates and adjusting scripts accordingly.
28	Aug 18 – Aug 24	Mid-collection review: Assesssing data diversity, volume, and quality across platforms.
29	Aug 25 – Aug 31	Updating and fine-tuning data collection scripts based on review insights, reinforcing error checks.
30	Sep 1 – Sep 7	Continuing collection; initiating exploratory analysis.
31	Sep 8 – Sep 14	Finalizing data collection; focusing on consolidating datasets from all sources.
32	Sep 15 – Sep 21	Initiating analysis and identifying key trends.
33	Sep 22 – Sep 28	Performing descriptive statistics, generating initial visualizations, and establishing baseline metrics.
34	Sep 29 – Oct 5	Applying text mining and sentiment analysis techniques; initiating preliminary topic modeling to uncover patterns.
35	Oct 6 – Oct 12	Refining text mining and sentiment analysis models; validating initial findings and adjusting parameters as needed.
36	Oct 13 – Oct 19	Expanding analysis scope: Incorporating network analysis and correlation studies to link trends across platforms.
37	Oct 20 – Oct 26	Developing advanced visualizations; creating detailed graphs, charts, and infographics to illustrate analytical insights.
38	Oct 27 – Nov 2	Integrating additional metrics; refining analytical models and visualization techniques based on feedback.
39	Nov 3 – Nov 9	Interpreting analytical outcomes; contextualizing findings with existing literature and documenting emergent
40	Nov 10 – Nov 16	Finalizing comprehensive analysis; compile a detailed summary of results.
41	Nov 17 – Nov 23	Begin report drafting: Outline structure and write the introduction and methodology sections.
42	Nov 24 – Nov 30	Drafting results and discussion sections; integrate visualizations; revise the overall report outline.
43	Dec 1 – Dec 7	Refining the report draft; incorporating feedback from peers and supervisor(s).
44	Dec 8 – Dec 14	Finalizing the full report draft; preparing presentation slides, polishing the narrative.
45	Dec 15 – Dec 21	Rehearsing the presentation; addresssing final revisions; preparing submission materials.
46	Dec 22 – Dec 28	Implementing final revisions, updating all documentation, and performing a final backup of the dataset.
47	Dec 29 – Jan 1	Final review; wrapping up the project, preparing the presentation.