Since the advent of social media, gathering what is trending in the social world has evolved into an invaluable tool for law enforcement. To get started, agencies should determine what role social media will play in their operations. This article presents recommendations for five of the best social media monitoring tools for law enforcement.
Law enforcement agencies need to be equipped with the best tools available to effectively monitor social media. In order to choose the right tool for your needs, it is important that you know what features you want and what each tool has to offer. This article covers eight of the most effective social media monitoring tools and provides key information about their capabilities and functionality.
OssaLabs
Law enforcement agencies who rely on social media intelligence are better equipped to build stronger ties with communities and their leaders, leading to safer, more secure communities.
The OssaLabs social media monitoring tool helps officers:
- Discover opportunities for positive outreach in the community
- Empathize with community members by learning more about their interests and views on important issues
- Identify new ways to connect with and protect vulnerable populations, such as homeless persons and at-risk youth
- Understand public perception during periods of heightened tensions to better prepare police teams and avert crises before it’s too late
The OssaLabs Solution
OssaLabs’ Social Impact Pro can assist law enforcement agencies in monitoring social media discussions to understand community attitudes before they become a news headline. This critical insight can help officers get in front of issues and address them more effectively.
- Discover conversations online and what is trending in the local population
- Collect data regularly on community attitudes and perceptions about local law enforcement and issues affecting the community as a whole
- Monitor public discussion to better engage communities on sensitive matters
- Scan the U.S. map down to the county-level to understand where topics are trending
Babel Street
Capabilities and data sources: Babel Street says that its web-based platform, BabelX, can conduct cross-lingual searches for more than 200 languages and automatically translate posts to English. Babel Street also claims that BabelX can analyze sentiment in over 50 languages.
Babel Street states that its data sources include:
- Over 30 social media platforms
- Proprietary third-party and customer datasets
- Billions of blogs and message boards
Known clients: The Los Angeles Police Department (LAPD) had a demo with Babel Street in May 2016. In its correspondence with the LAPD, Babel Street stated that it worked with many U.S. law enforcement agencies and fusion centers.
News stories that include information about Babel Street clients:
- The Department of Homeland Security reportedly used Babel Street and evaluated its products as part of its System Assessment and Validation for Emergency Responders (SAVER) Program in July 2016
- The FBI
- The Department of Justice granted Babel Street a $500 million Blanket Purchase Agreement in 2019
- U.S. Military Operations Command
- The Seattle Police Department held a two-month trial of Babel Street in 2016
Digital Stakeout
Capabilities and data sources: Digital Stakeout advertises that it has integrated its tools into its Scout product, which uses data from the Internet, social media, and the dark web to provide users continuous data discovery, user-defined alerts, and custom data visualizations on its platform.
In 2016, Digital Stakeout claimed that its Canvass tool had access to over 550 social networking sites in over ten categories. On its website, Digital Stakeout states it collects information from Twitter, Reddit, Tumblr, Disqus, and WordPress.
Known clients: Digital Stakeout’s clients have included:
Skopenow
Capabilities and data sources: Skopenow claims that its tools have the capacity to:
- Automatically find, extract, and analyze data from social media anonymously
- Conduct behavioral recognition analysis through image and text analysis, subject monitoring, and comprehensive search results
- Notify users via automated alerts when there are developments in a subject they are tracking
- Create interactive visualizations by merging location data from consumer reports, social media posts, and metadata
Skopenow claims to have access to:
- All major social media platforms, including Parler
- Court records through PACER
- Consumer records
- Phone numbers, usernames, email addresses, court records, and any information available online
Making Algorithms Publicly Accessible
The potential for prejudice in social listening initiatives points to a broader problem common to many analytics initiatives: The algorithms employed are often not publicly available and are thus not subject to challenge from residents. Usually, cities employ algorithms developed by private contractors who are unwilling to release detailed information for fear of revealing proprietary information to competitors, and cities are hesitant to release source code because of concerns over cybersecurity and opportunities to game public systems. Citizens are therefore unable to assess the impartiality of the tools used to target people as potential criminals.
A lack of access to algorithms is a problem not only for ensuring equity, but also for confirming that the information gathered is accurate. The experience of Fresno City Councilmember Clinton Olivier shows that social media mining can be a misleading source of information. Representatives from Beware — a company that produces a social listening software that assigns residents and properties threat levels of green, yellow, or red based on their social posts — presented their product to the Fresno City Council. During the course of their presentation, Council Member Clint Olivier asked the company reps to look up his threat level. His property showed up yellow.
In this case, Olivier’s property appeared as risky because Beware analyzes addresses based on seven-year periods, and former occupants may have had a criminal history. However, social mining may also miscategorize residents based on hyperbolic or sarcastic posts or even by misidentifying them completely. Similar algorithmic tools — like one that predicts recidivism risk based on resident data — have proven no more accurate than non-expert assessments.
One could imagine these cases of mistaken identity leading to unnecessary escalation. As Olivier colorfully explained, “even though it’s not me that’s the yellow guy, your officers are going to treat whoever comes out of that house in his boxer shorts as the yellow guy.”
Thinking that Olivier was potentially dangerous, officers could use excessive precaution and force, creating an unnecessarily tense situation.
According to Kortz, in order to ensure due process, governments need to make their algorithms available to residents in one way or another.
“To be able to challenge algorithms, they need to be auditable in some sense,” he explained. “While companies shouldn’t have to reveal their algorithms in their entirety, they should have to log certain steps. It’s best to require private companies to output an auditable trail.”
Cate echoed these sentiments, arguing “There needs to be redress. If you’re going to act on data, you need a way to let residents challenge your process.”
And, like Kortz, he does not think that revealing algorithms in their entirety is the answer.
“Revealing algorithms tells the public close to nothing,” he said.
Rather, he calls for governments to publish information on the quality of the underlying data and the effectiveness of the predictions based on independent tests. Like Kortz, he calls for a balance between the privacy of proprietary algorithms and the right to transparency.
New York City has recently started the process of institutionalizing algorithmic transparency, convening a task force to develop recommendations to the mayor on how agencies can reveal information on algorithmic tools without sacrificing proprietary secrets or cybersecurity. Last month, Stephen Goldsmith and I wrote an article for CityLab detailing transparency requirements the city should consider. Three relevant to social mining initatives are:
- Sharing the motivation for using an algorithm in order to provide residents a benchmark by which to evaluate results and allow them to assess intentions.
- Explaining what data went into the model and why to allow the public to identify potential bias from data tainted by historically discriminatory practices.
- Publishing performance data so citizens can assess the effectiveness of practices.
By opening social mining efforts to public redress, cities allow residents to understand, critique, and in some cases even improve these initiatives.
Conclusion
The following list is a comprehensive comparison of the top free and commercial social media monitoring tools currently available for law enforcement agencies and departments.