Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities

by   Nabil Hossain, et al.

Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those activities at times in the past or future, and self-reports made at the immediate time and place the activity occurs. Activities, such as alcohol consumption, may occur at different places and types of places, and it is important not only to detect the local regions where these activities occur, but also to analyze the degree of participation in them by local residents. In this paper, we develop new machine learning based methods for fine-grained localization of activities and home locations from Twitter data. We apply these methods to discover and compare alcohol consumption patterns in a large urban area, New York City, and a more suburban and rural area, Monroe County. We find positive correlations between the rate of alcohol consumption reported among a community's Twitter users and the density of alcohol outlets, demonstrating that the degree of correlation varies significantly between urban and suburban areas. While our experiments are focused on alcohol use, our methods for locating homes and distinguishing temporally-specific self-reports are applicable to a broad range of behaviors and latent states.


Building Dynamic Ontological Models for Place using Social Media Data from Twitter and Sina Weibo

Place holds human thoughts and experiences. Space is defined with geomet...

Bot Electioneering Volume: Visualizing Social Bot Activity During Elections

It has been widely recognized that automated bots may have a significant...

25 Tweets to Know You: A New Model to Predict Personality with Social Media

Predicting personality is essential for social applications supporting h...

A Graph Approach to Simulate Twitter Activities with Hawkes Processes

The rapid growth of social media has been witnessed during recent years ...

Macross: Urban Dynamics Modeling based on Metapath Guided Cross-Modal Embedding

As the ongoing rapid urbanization takes place with an ever-increasing sp...

ALCNN: Attention-based Model for Fine-grained Demand Inference of Dock-less Shared Bike in New Cities

In recent years, dock-less shared bikes have been widely spread across m...

Where You Are Is What You Do: On Inferring Offline Activities From Location Data

Studies have shown that a person's location can reveal to a high degree ...

Please sign up or login with your details

Forgot password? Click here to reset