Skip to main content


          “Countering -Fake News in Natural Disasters via
            Bots and Citizen Crowds”

                      By Tonmona Roy
Countering Fake news During natural disasters

On September 20, 2017, Mexico City was hit with a 7.1 magnitude earthquake, killing hundreds of people. The death toll started rising quickly, with people trapped under the debris of the fallen buildings. When there is a catastrophe of this magnitude, it is hard for the government to quickly assist everyone.  Many started using social media to spread news about trapped people and supplies needed. Among the social media platforms, Twitter became the main site for exchanging information and mobilizing citizens for action. People started using hashtags to learn about what was happening in their neighborhoods and direct actions they could take to help. Some of the most popular hashtags used were #AquiNecesitamos (#HereWeNeed), #Verificado19S (#Verified19S, [19S represents September 19th, the day of the earthquake]). With these hashtags people started to post what they needed and where to deliver it.

Fake tweetHowever, misinformation started spreading. Some citizens, e.g., started tweeting and calling for help for a doctor allegedly trapped in a building.
But Dr. Elena Orozco, her friends and family all suddenly started reporting on social media:
“…Elena Orozco is not trapped in any building. She is right here with us. She was trying to rescue her co-workers, who were the ones trapped in the building. We are actually still missing Erik Gaona Garnica who decided to go back into the building to get his computer…”

Systems for Countering-Fake News Stories

Given that Fake News was critically affecting the rescue and well being of people we decided to do something about it. We quickly realized that Codeando Mexico (a social good startup) and universities across Mexico, such as UNAM, were organizing crowds of citizens to build civic media to help the earthquake. Our research lab (The HCI lab at West Virginia University) thus decided to unite forces and in a weekend we had rapidly built together a large scale system to counter fake news and bring verified news about the earthquake.
This led us to decide to bootstrap on existing social networks of people to solve the cold-start problem. Through our investigations, we identified that citizens had put together a Google Spreadsheet where they were posting news reports about the earthquake that were 100% verified (they had a group of people on the ground who actively verified each news report.) The group would then manually post on their social media accounts the verified news from the spreadsheet. But, as the group became more popular, it was hard for the volunteers to spend more time on it and coordinate.

Bootstrapping Bots on Networks of Volunteers

Our second design focuses on automating some of the critical bottlenecks that these networks of volunteers experienced when verifying news. In our interviews, we identified that it was difficult for volunteers to differentiate fake and real news because it involved gathering all of the facts behind the story; and it was also a pain to share on social media the news. Our second platform therefore introduced the idea of leveraging citizen crowds and bots (such as our bot @FakeSismo). Bots help in the verification of news by gathering facts and then massively sharing the verified news stories on social media, along with an automatically generated image macro that helps to give more visibility to the story. In this way, human volunteers can focus more on verifying the information. The work flow of our system is as follows:

Bots in Action

To test out our bot, it started by tweeting verified information about the resources needed and it got very good responses. The bot currently has 176 followers and it’s increasing. Example tweetAs an example, the bot posted a news report about needing certain resources and someone started engaging with the bot, saying they had a refrigerator to give away. The bot focused on distributing the information and connecting the citizen who could use the refrigerator.
We also saw that citizens tried to actively verify news reports along with the bot.

In short, our bot is working together with a group of enthusiastic volunteers and helping in gathering and distributing verified information. As we test out more of the bot, we hope to connect with a larger mass of people to start a platform that can counter-fake news during natural disasters.

Social Media, Civic Engagement, and the Slacktivism Hypothesis: Lessons from Mexico's "El Bronco''

Does social media use have a positive or negative impact on civic engagement? The cynical "slacktivism hypothesis'' holds that if citizens use social media for political conversation, those conversations will be fleeting and vapid. Most attempts to answer this question involve public opinion data from the United States, so we offer an examination of an important case from Mexico, where an independent candidate used social media to communicate with the public and eschewed traditional media outlets. He won the race for state governor, defeating candidates from traditional parties and triggering sustained public engagement well beyond election day. In our investigation, we analyze over 750,000 posts, comments, and replies over three years of conversations on the public Facebook page of  "El Bronco.'' We analyze how rhythms of political communication between the candidate and users evolved over time and demonstrate that social media can be used to sustain a large quantity of civic exchanges about public life well beyond a particular political event.

Read more about our research: here
Spanish Version of Paper: here
Photo of El Bronco winning the election

Visualizing Targeted Audiences

Social Query InterfaceUsers of social networks can be passionate about sharing their political convictions, art projects or business ventures. They often want to direct their social interactions to certain people in order to start collaborations or to raise awareness about issues they support. However, users generally have scattered, unstructured information about the characteristics of their audiences, making it difficult for them to deliver the right messages or interactions to the right people. Existing audience-targeting tools allow people to select potential candidates based on predefined lists, but the tools provide few insights about whether or not these people would be appropriate for a specific type of communication. We have introduced an online tool, \textit{Hax}, to explore the idea of using interactive data visualizations to help people dynamically identify audiences for their different sharing efforts. We are providing the results of a preliminary empirical evaluation that shows the strength of the idea and points to areas for future research.

Directed Social Queries With Transparent User Models

The friend list of many social network users can be very large. This creates challenges when users seek to direct their social interactions to friends that share a particular interest or have the potential of being able to answer a particular question. We are presenting a self-organizing online tool that through incorporating ideas from user modeling and data visualization allows a person to quickly identify which friends’ best match a social query, enabling precise and efficient directed social interactions. To cover the different modalities in which our tool might be used, we are introducing two different interactive visualizations. One view enables a human-in-the-loop approach for result analysis and verification, and, in a second view, location, social affiliations and "personality" data is incorporated, allowing the user to quickly consider different social and spatial factors when directing social queries. We are reporting on a qualitative analysis, which indicates that transparency leads to an increased effectiveness of the system. This work contributes a novel method for exploring online friends.

Location Based Context Aware Recommendation System

Visual of I'm Feeling Loco Recommendation System Research in ubiquitous location recommendation systems has focused on automatically inferring a users’ preferences while little attention has been devoted to the recommendation algorithms. Location recommendation systems with a focus on recommendation algorithms generally require the user to complete complicated and time-consuming surveys and rarely consider the users current context. The purpose of this investigation is to design a more complete ubiquitous location-based recommendation algorithm that, by inferring users’ preferences and considering time geography and similarity measurements automatically, betters the user experience. Our system learns user preferences by mining a person’s social network profile. The physical constraints are delimited by a user’s location, and form of transportation, which is automatically detected through the use of a decision tree followed by a discrete Hidden Markov Model. We defined a decision-making model, which considers the learned preferences, physical constraints and how the individual is currently feeling. Our recommendation algorithm is based on a text classification problem. The detection of the form of transportation and the user interface was implemented on the Nokia N900 phone, the recommendation algorithm was implemented on a server that communicates with the phone. The novelty of our approach relies on the fusion of information inferred from a user’s social network profile and his/her mobile phones sensors for place discovery.

Enchantment Under the Sea: An Intelligent Environment for Music Mixing

Disc jockeys generally mix music in a confined isolated space. This can make the DJ have depression sentiments and it can also impact the DJ's understanding of his public. We present Enchantment Under The Sea, a new intelligent environment that allows DJs to roam freely, interact directly with his audience, receive informative feedback about the public's social interactions, while respecting the DJ’s privacy concerns. The music interface is controlled using Microsoft’s wireless touch mouse with ubiquitous gestures that resemble dance moves. The music mixing interface is displayed on the walls of the event, where two different display modalities are enabled: open interface, in which the public can observe all of the DJ’s decisions with the music mixing interface and also actively give music suggestions to the DJ, and a closed interface, where all the music controllers are mapped to sea animals, that only the DJ knows the mapping to, thus providing privacy to the DJ’s work. The public's social interactions are measured with sonar sensors whose data is provided to the DJ through the musical interface. We will report results of a controlled usability inspection.

Mmmmm: A Multi-Modal Musical Mobile Mixer

Mock-Up of Multi-Modal Musical Mobile Mixer Mmmmm stands for multi-modal musical mobile mixer and it is a new musical DJ application that has been designed for the Nokia N900 phone. This software enables a new type of interaction between the DJ and the crowd. Mmmmm has streaming via Bluetooth of the music generated in the phone to wireless speakers, which allows the DJ to move about the environment and get familiarized with the crowd, turning the experience of DJing into an interactive and audience-engaging process. It also has eyes-free mode enabled and it is carried out through hand gestures and haptic feedback, which allows the DJ to focus on the public and have a better intuition of what they are feeling. Mmmmm also has a "party detection" mode. By using the phone camera, the party scene is scanned and then a certain type of music as well as a series of songs is suggested. This mode helps novice DJs to instantly have a much better music repertoire, creating the illusion that an expert DJ is selecting the music the crowd is dancing to. Eyes-free mixing can be enabled at the wish of the user.

Wii Remote for Arm and Risk Therapy in Stroke Survivors

The Nintendo Wii remote is a compact, readily accessible position, orientation and motion-sensing technology with blue tooth wireless communication. We integrated the 3D position sensor to a gesture therapy system of computer simulated therapy exercises, which had previously been developed. We have also used pitch, yaw and roll, from the Wii remote 3D accelerometer to navigate a fly-through of an arbitrary Direct-X generated terrain. The hardware-based orientation and motion-sensing capabilities of the Wii remote complement vision-based systems and interface well with wrist exercises. The Wii remote model is promising for integration into clinical and home-based rehabilitation exercise therapy systems.

Time Scheduling Using a Genetic Algorithm

Photo of the School of Engineering of the National Autonomous University of Mexico The School of Engineering of the National Autonomous University of Mexico has more than 10,000 students. This large number of students requires the availability of a large number of courses. How do you schedule these courses in the most optimal way? What criteria would you use to say that one solution was better than the other? We proposed to solve this problem by means of a genetic algorithm. What we optimized was the time a classroom was utilized, i.e., we wanted to have continuous classes assigned to it throughout the day and know the schedule of each course. We considered that an optimal solution would have courses from the same semester and the same career assigned in rooms that were nearby to each other and in contiguous schedules since this would be beneficial for the students. In our proposed solution we also took into consideration the problem of having two different courses assigned in the same time slot or one professor assigned to two different courses in the same time period.