This is a great question, Sandra!

As an exploratory tool, SensePlace2 is about hypothesis generation and decision-making. This means there isn’t one “this software answers this specific question about a certain topic” answer.

What the software does allow us to do is to lean how topics and the conversations around topics are spatialized, and how the geography inherent within Tweets unfolds over time. For example, the countries and places mentioned alongside ‘Earthquake’ will have very different spatial and temporal patterns than Tweets discussing “Smallpox.”

By allowing analysts to investigate trends in topics they’re interested in, SensePlace2 helps inform and guide the thinking process. For instance, a faculty member studying infectious diseases wanted to see how mentions of the disease in SensePlace2 coincide with known settlement patterns of the at-risk population. Without identifying which places are mentioned in Tweets also containing mentions of the disease, she wouldn’t be able to do this. After all, every event happens at a place and time, so communicating the spatiotemporal landscape surrounding an event contributes a much needed piece of the analytical puzzle.

We hope that this kind of querying and investigation will enrich the way scientists think about the problems they’re trying to solve.

This is somewhat difficult to answer, for two reasons. First, the spatial and temporal patterns associated with key terms varies considerably from one term to the next. This means that the impacts of understanding these patterns will be different depending on the phenomena under investigation. Second, the potential results of exploratory analysis are always hard to predict. The knowledge generation and discovery process that is afforded by freely querying and filtering information depends on the analyst, the topic, and the problem one is trying to solve.

The good news is that we know from social science literature that many phenomena behave similarly and are discussed similarly in social media. This means we can reveal key differences between how events like natural hazards and violent conflicts rely on place mentions and social media use. As an example, less than 1% of Tweets from all topics combined make use of the geolocation feature in Twitter. However, when filtered to natural disasters such as earthquakes, that number increases to nearly 15%. So there is a clear relationship between the type of event being Tweeted about and how important “place” as a concept is to users.

This is a very valid question! The applications you mention are some of the most common examples (especially government response, as this project is funded by the department of Homeland Security). Twitter is much larger than outbreaks and protests though!

One thing we are looking at is how mentions of places correspond to people actually moving to/from those places. So in addition to the things I’ve mentioned in my responses to Jeffrey and Sandra, we can also identify when the same users Tweet from different locations and are therefore “Tweeting on the move” or Tweeting about a recent relocation (be it leisurely travel or in a refugee situation). By pairing social media use with human mobility patterns, we can begin to answer all sorts of interesting questions about why people move, when they do it, and how they communicate about these events.

Hi Aurora,

My contribution is in two parts. On one end, I help guide the interface design and use of cartographic technology in the system (two examples are provided in the poster view regarding UI layout and new symbology types). I’m very interested in usability and ensuring that the way the system performs is consistent with the tasks users are trying to accomplish, so I am constantly pushing to make the UI as intuitive and usable as possible. On the other end, as an IGERT trainee in Big Data Social Science, I am working to expand the capabilities of the system to handle larger amounts and different kinds of data. At present I am building a database of flight records that will be brought into the system to identify flights, airports, and airlines that are discussed in Tweets (Twitter is full of both excited and unhappy travelers). Using this database alongside Tweets, we can show the exact aircraft and flight routes being discussed, when they were discussed, and in which context the discussion occurs. The flight data allows us to identify delays, cancellations, and other deviations from the scheduled plan. From this, we can begin asking questions about how delays influence social media activity, what travelers Tweet about before, after, and even during the flight (if WiFi is available). This helps us paint a much more dynamic picture of the patterns that exist between human mobility and social media use.

Thanks, Gary!

Indeed we have many discussion about national security implications. This project is funded by the Department of Homeland Security (DHS) and the US Army Corps of Engineers (USACE) through an initiative called VACCINE (Visual Analytics for Command, Control, and Interoperability Environments). The driver behind the software and its use of social media began by the need these agencies have for determining how social media are used during crisis events and disaster relief efforts.

Due to the generalizability of the system and its functions, we continue to work closely with DHS and USACE to meet their objectives, while at the same time expanding the capabilities of SensePlace2 more broadly within the realm of social science (as in the airline example in my previous response).

Great question! Ground truth is tricky since computationally, context is very difficult to determine (especially with social media being notoriously sarcastic, snarky, and prone to puns). To account for this, we’re beginning to evaluate the performance of our system by comparing it to the success of humans, who are really good at picking out the proper context. We’re having participants read a set of Tweets and decide which places are being discussed, and then seeing how well the software can do the same.

This is an important task because as your question brings up, there are indeed some cases that are more difficult than others. A simple example is “Lansing.” Is the user talking about Lansing, MI or Lansing, IL? We’re likely to guess the former, but it takes previous knowledge (e.g., knowing one is more popular, a state capital, etc) and experience to make that call. A similar example is “Georgia,” which is both a state and a country. Other cases that pose a challenge are “Main St” and place names that are common to many cities.

Our granularity is at the sub-city level (per above, streets are difficult without additional context, which we can determine if it is available). Using the Geonames database, which works with nested indices (e.g., Central Park is within Manhattan, which is within New York, New York, USA), we are able to determine the places mentioned at several levels; we choose and map the most granular. In cases where only the country is mentioned, the centroid is used instead.

Absolutely! The connecting lines identify two (or more) places that are mentioned in the same Tweets. For example, If I Tweet “Leaving State College for Zurich”, two points would be drawn on the map – one for each location. Hovering the mouse over either point would show a connection to the other, and highlight my Tweet in the Tweet list (inversely, hovering the Tweet would highlight the points and the connection between them).

So it is making an association between the context of the Tweet. We also have a function that shows “about-from” relationships. This reveals Tweets about one location that were made from another. Since users rarely enable location tracking and less than 1% of all Tweets record the sender’s lat/lon, this doesn’t capture a very large spectrum, thus our focus is on establishing the context inherent, but previously invisible, in the vast majority of Tweets.