Social Media is commonly used by policing organisations to spread the word on crime, weather, missing person, etc. In this work we aim to understand what attracts citizens to engage with social media policing content. To study these engagement dynamics we propose a combination of machine learning and semantic analysis techniques. Our initial research, performed over 3,200 posts from @dorsetpolice Twitter account, shows that writing longer posts, with positive sentiment, and sending them out before 4pm, was found to increase the probability of attracting attention. Additionally, posts about weather, roads and infrastructures, mentioning places, are also more likely to attract attention. http://people.kmi.open.ac.uk/miriam/publication/FernandezSocInfoCityLablWs2014.pdf

1. Policing Engagement via Social
Media
Date: November 2014, SocInfo
Author: Miriam Fernandez, Amparo Elizabeth Cano, Harith Alani

2. Context
• Policing organisations use social media to
spread the word on crime, severe weather,
missing people, …
• Many forces have staff dedicated to this
purpose and to improve the spreading of key
messages to wider social media communities
• Research shows that exchanges between
police and citizens are infrequent

3. Goal
• Understand what attracts citizen’s to social
media policing content
– What are the characteristics of the content that
generate higher attention levels
• Writing style
• Time of posting
• Topics
– Help police forces to identify actions and
recommendations to increase public engagement

4. Approach
• @Dorsetpolice Twitter account
– 3,200 posts [2011-12-23 / 2014-06-12]
• Announcements, appeals, crime reports, etc.
• Retweet as engagement indicator
– 2,270 (86%) seed vs. 430 non-seed posts
• Seed post = received at least one retweet
• Balance dataset for the analysis
• ML & Semantic techniques to analyse engagement

5. ML Analysis
• Content Analysis [Rowe & Alani 2014]
– Top discriminative features of retweeted vs. non
retweeted posts
– Top discriminative features of highly retweeted
posts
• Tweets that generate higher attention
– Are longer
– Have positive sentiment
– Mention other users
– Are posted between
8:00 a.m 16:00 p.m
[Rowe & Alani 2014]. Mining and comparing engagement dynamics across multiple
social media platforms. WebScience 2014

6. Semantic Analysis
• Annotate tweets with entities & concepts using
TextRazor
– DBPedia & Freebase as knowledge bases
• Seed posts talk about
– weather, roads and infrastructures & mention locations
• Non seed posts talk about
– crimes such as burglary, assault or driving under the
influence of alcohol.

7. Conclusions
• Use a combination of ML and semantic
techniques to understand the most
discriminative language, time features and
topics of those tweets generating higher
attention levels towards policing content
• Preliminary study
– One social media platform (Twitter)
– One police force(@dorsetpolice)
– One engagement indicator (retweets)

8. Questions?