System Demo: Demo link
System Component Details
Data Collection - Data Sources and Apache Kafka Framework:
CitizenHelper uses an opensource distributed computing platform
to collect data (see Figure1), which provides flexibility to scale producers (information sources), and consumers (information processors), in addition to a streaming data buffer---valuable for slow downstream processors when needed. System currently supports realtime data collection using Streaming and Location APIs of Twitter, as well as Instagram and Facebook (for public groups and pages), which are useful during humanitarian disasters for situational awareness information collection.
Additionally, the system supports collection of news (including GDELT) and blogs streams as well as data from Web knowledge bases including Wikipedia, and OpenGov Data.
Metadata Processing - Spark Streaming, Application Web Server and Analytics Services:
The proposed system connects data collection components from to processors in opensource stream computing framework Apache Spark.
Different processors perform analytics on the streamed content by leveraging various analytics services, to extract and associate enriched metadata such as information provider classification (e.g., gender, user type such as organization), content classification for topics, intent, etc.
Data Storage and Visual Dashboard:
CitizenHelper stores raw data in a file system for long-term archiving, and processed data with extracted metadata in a database
which supports a frontend visualization dashboard Kibana for streaming analytics. Our visual dashboard is composed of different analytical widgets, such as volume trend graph of Twitter posts (tweets) over time. %top active users with tweet frequency corresponding to a topical hashtag, and so on.
These widgets have two unique features. First, when a user interacts with a widget and modifies an analysis unit on the widget (e.g., time slice on a trend graph, region of interest on the map, topical tag in the word cloud list), then all analytical widgets get updated corresponding to that change in the analysis unit. Second, the visual dashboard supports collaborative teamwork by allowing saving and sharing of a state of the dashboard by an end user, which in turn allows another collaborating team member study the same set of analyses from his/her colleague. Also, these widgets can be repositioned and deleted as needed to avoid visual information overload. System details with exemplary analyses and demos are available at the demo link, based on prior interactions with the analysis widgets.
Usage
Explore each widget by interacting with that for fine-grained analysis of an event or a topic, such as selecting the timeline for a specific period will render all other widgets accordingly. Widgets include:
- Volume Timeline: shows Volume trend for engagement in this topic. Select a timeline slice to analyze, by mouse over selection.
- Activity Timeline: shows top Twitter users over time who engaged in this topic.
- Tweet Cloud: shows tweet summaries for analyzing public concerns and reactions for this event, by user types.
- User Cloud: shows user profile summaries for analyzing the participating demographics in discussions of this topic, by user types (e.g., organization).
- Activity Tile Map: shows participation of users across geographical locations. Select a location for constraining analysis in other widgets.
- Open Data Map: shows displacement data statistics across the world, to inform comparative analysis of user engagement in the concerned locations.
- Tweet Stream: shows specific set of tweets with frequency for the selected constraints of analysis.
- User Graph: shows user engagement frequency, to identify actively engaged users.
Future Analytics: user type (gender, user affiliations with organizations), emotion with concerned topics, organization network analysis, etc.
B. ANALYSIS EXAMPLES FOR HUMANITARIAN ORGANIZATIONS
1. Demographics - Content Practices of Specific User Identities for Gender-Violence Events
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| (Figure 2.a.) Organizations tweet about husbands | (Figure 2.b.) Tweets by individual users describing themselves as husbands |
Figure 2. Husband Portrayal:
Individual identity user accounts who identify themselves as
husbands in user profiles write pro-women tweet content, whereas when
Organization identity user accounts describe Husbands
they are portrayed as threats. This data was
collected for the domain of anti-gender based violence over the time
period of Aug 4th 2016 to Aug 28th 2016.
2. Narratives of Diverse Sources - Analysis for Gender-Violence Events
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| (Figure 3.a.) Editorial-news Content Summary | (Figure 3.b.) User-generated Content Summary |
Figure 3. Our tool allows us to compare the
topics related to our research that are currently being covered by
the world news vs those that people are talking about on twitter. In
this figure, we observed the diverse nature of narratives being
promoted on news media, in contrast to diverse types of issues being
carried over under the activism for anti-gender violence, during the
period of Aug 4th 2016 to Aug 28th 2016.
3. Temporal Diffusion - Analysis for Gender - Topic activity over time for Gender-Violence Events
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| (Figure 4.a.) Tweets by Topic | (Figure 4.b.) Tweets over time |
Figure 4. Our tool allows us to view the
breakdown of topics in the current stream of tweets for a specific
domain. In this figure, we observe the trend of number of tweets by
topic vs the total number tweets for the domain anti-gender based
violence over the time period of July 31st 2016 to September 1st
2016.
4. Geographical Engagement - Awareness Analysis for Gender-Violence Events
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| (Figure 5.a.) Tweet count by location | (Figure 5.b.) Gender based violence counts provided by open data from FBI UCR |
Figure 5. The visual tool allows us to view
tweets by originating location, which indicates the user
participation and awareness of concerned issues. In this figure, we
observe the variation in number of tweets indicating social awareness
by location for the time period of Aug 4th 2016 to Aug 28th 2016, and
the contrasting pattern of 2014 GBV related
reports by location in the FBI Uniform Crime Record data.
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| (Figure 6.a.) Tweet count by location | (Figure 6.b.) Global Displacement counts provided by open data from IDP |
Figure 6. The geographical contrast analysis
capability for another humanitarian issue of Global Displacement. In
this figure, we observe the variation in the number of tweets
originating by location -- indicating social awareness and reporting
for the issue -- during the period of Jan 5th 2017 to Feb 23rd 2017,
and in contrast to the reported displacement by IDP.
5. Hashtag analysis - #likeagirl
- #likeagirl - Women are portrayed in a positive light using this hashtag, and both individuals and organizations have endorsed it. It is likely because of pro-women start for this movement by an organization - Tag cloud of hashtags shows the endorsements of other communities and related initiatives in this movement.
- Reference about #LikeAGirl 1
- Reference about #LikeAGirl 2
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| (Figure 7.a.) Hashtags Mentioned by Individuals | (Figure 7.b.) Hashtags Mentioned by Organizations |
