Academic research > Research areas
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Research areas
Academic researchers can use Twitter data to study nearly any topic, across nearly any discipline. If working with social media data is your specialty, then you might be interested in browsing some of Twitter’s research goals.
These are some of the biggest questions and challenges that teams at Twitter are working through today. We believe that more scholarship on these topics, whether from Twitter, or from academic researchers around the world, can help make the world a better place.
While this list of topics is not exhaustive, these are areas where Twitter is keen to learn more from the academic community’s expertise. For your consideration, we’ve shared sample research objectives related to the topic, as well as some high-level reference materials to explore.
Promote healthier online conversations
Identify ways to improve experiences on Twitter and promote healthy online behavior.
Example Research Objectives
Research and applied methods to reduce and halt online abuse and hate speech
Research or data modeling methods that identify toxicity and unhealthy Tweets in non-English languages
Understand the experience, harms and effects of targeted or coordinated online abuse or harassment (e.g. such as with doxxing, dogpiling)
Trends or variances in online abuse or harassment across the world and/or in times of conflict
Increase understanding of polarizing or controversial topics, and receptivity to new or contradictory information
Examine government and elected official responses and its impact on civil discourse (e.g., in public health crisis, in times of public unrest)
Study immediate and lasting impacts of Twitter feature changes (e.g., who can reply to a Tweet)
Reference materials from Twitter
- Experiment results in new conversation settings, coming to a Tweet near you
- Introducing Birdwatch, a community-based approach to misinformation
- How Twitter is working to combat the ‘shadow pandemic’ of violence against women
- How Twitter is amplifying #SuicidePrevention resources on the platform
- How HateLab at Cardiff University uses Twitter data to combat hate speech [case study]
- Twitter rules for platform safety
Responsible machine learning at global scale
Build responsible, responsive, and community-driven machine learning methods, systems, and applications for use.
Example Research Objectives - Machine learning systems/methods
NLP methods for extracting entities and/or micro-topics from Tweets
NLP techniques to identify and validate sentiment in all languages
Modeling techniques for identifying toxicity in Tweets, images, and audio in non-English languages
Applications of deep learning or convolutional neural networks to natural language processing
Graph topology methods (e.g. how a network’s shape determines the spread of information, comparing diffusion networks)
Example Research Objectives- Applied machine learning/data science
Research and development of responsible machine learning systems, such as: systems for removing bias, content amplification, procedural justice
Techniques for greater fairness and remove bias in natural language, media, images, and audio classifiers
Representation learning data efficiency in model training and inference, including: self-supervision, pre-training, reduced manual data labeling
Techniques to improve real-time recommendation systems with active, continual, reinforcement learning
Techniques to improve recommendation systems with an implicit feedback loop, casual reinforcement learning
Modeling methods to group and identify user interests by behavioral and contextual signals to improve real-time recommendation systems
Reference materials
- Deep learning on dynamic graphs
- Introducing VMAF percentiles for video quality measurements
- Graph ML at Twitter
- Distributed training of sparse ML models - Part 1, Part 2, Part 3
- Twitter meets Tensorflow
- Twitter learnings from the Recsys 2020 Challenge
- Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis [research study]
- Transferability of Spectral Graph Convolutional Neural Networks [research study]
- Neural 3D morphable models: spiral convolutional networks for 3D shape representation learning and generation [research study]
- Addressing delayed feedback for continuous training with neural networks in CTR prediction [research study]
- Discriminative topic modeling with logistic LDA [research study]
- Doubly Robust Join Learning for Recommendation on Data Missing Not at Random [research study]
- CARL: Aggregated Search with Context-Aware Module Embedding Learning [research study]
- cRNA classification with graph convolutional networks [research study]
Identify and prevent platform manipulation
Improve detection and prevention of malicious automated activity, misinformation, disinformation, and information operations.
Example Research Objectives- Misinformation or disinformation
Research and models that identify the reach and potential for harm of misleading information campaigns or groups on Twitter across the globe
Research and models that identify the reach and impact of various misinformation operations across the globe
Detection methods and identification of coordinated manipulative behavior (e.g., information operations or state manipulation)
Examine government and elected official responses and their impacts on public discourse (e.g., how official responses impact perceptions of public health crisis)
Techniques for the identification of misleading information at global scale, in non-English languages
Reviews of current Twitter policy and enforcement tactics and its impact on platform manipulation practices
Example Research Objectives- Automation detection
Identification methods for spam, filtering methods for spam
Identification methods for malicious bots/automated account activity
Delineation between automated behavior and genuine users (e.g., new accounts, grassroots movements)
Identification methods for automated activity that is beneficial vs. harmful, manipulative
Impact analysis around Twitter policies regarding platform manipulation and spam
Reference materials
- Insights from the 17th Twitter Transparency Report
- What to expect on Twitter on US Inauguration Day 2021
- Twitter's work around the 2020 US Elections
- Bot or not? The facts about platform manipulation on Twitter
- Fake News Detection on Social Media using Geometric Deep Learning [research study]
- Twitter Transparency Reports: Information operations data archive
- Twitter Transparency Reports: Platform manipulation
- Twitter Transparency Reports: Rules enforcement
- Twitter Developer Policy: Keeping bot accounts compliant, How to quickly update your bot profile bios
- Twitter platform manipulation and spam policy
Identify market changes from conversations
Identify how conversations on Twitter impact different industries, markets, and future trends.
Example Research Objectives
Techniques for extracting entities and/or micro-topics from Tweets for predictive analysis, futurecasting
Prediction models assessing features that impact reach and engagement with Tweets
Applications of Twitter data in support of business operations (e.g., supply chain insight, predictive analytics, forecast revenue)
Applications of Twitter data for decision-making related to brand reputation, new market penetration, behavioral economics research
Reference materials
- Does Twitter chatter matter? Online reviews and box office revenues [research study]
- Twitter mood predicts the stock market [research study]
- Wisdom of Crowds: Cross‐Sectional Variation in the Informativeness of Third‐Party‐Generated Product Information on Twitter [research study]
- Related case study: LikeFolio uses Twitter data to gain unique financial insights for investors
- SMA Futures Data Sanities: the predictive nature of our Futures Twitter based sentiment factors
- Riskante Retweets: „Predictive Risk Intelligence“ [research study] [German]
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If it’s being talked about in the world, it’s probably being talked about on Twitter. Academic Research access grants access to historical and real-time public Twitter data, helping advance research objectives for nearly any discipline.