Using AI To Prevent Gang Violence by Analyzing Tweets

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Apr 26, 2020
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Using AI To Prevent Gang Violence by Analyzing Tweets

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Applying AI and machine learning to understand gang language and detect threatening tweets related to gang violence.

Execution time: 0.0015 seconds

Execution time: 0.0003 seconds

The problem

“Some believe that bolstering school security will deter violence, but this reactionary measure only addresses part of the problem. Instead, we must identify threats, mitigate risk, and protect children and staff before an act of violence occurs.” — Jeniffer Peters, Founder of Voice4Impact (Project Partner)

Chicago is considered the most gang-infested city in the United States, with a population of over 100,000 active members from nearly 60 factions. Gang warfare and retaliation are common in Chicago. In 2020 Chicago has seen a 43% rise in killings so far compared to 2019.

The solution

It was noticed that gangs often use Twitter to communicate with fellow gang members as well as threat other gang members. Gang language is a mixture of icons and some gang terms.

 

Sample Gang Language


The project team split the work into two parts:

  • Implement a machine-learning algorithm to understand gang language and detect threatening tweets related to gang violence.
  • Find co-relation between threatening tweets and actual gang violence.

Part 1: Detecting violent gang language and influential members

The goal was to classify tweets as threatening or non-threatening so that the threatening ones can be routed to intervention specialists who will then decide what action to take.

Step 1: Labeling tweets collaboratively

First, a tool was created to label tweets faster and train the machine learning model. We were only provided the raw tweets. Searching the web, we found LightTag, which is a product designed for exactly this but it is a paid product once you exceed the comically low number of free labels.

We needed a simpler solution that does everything we need, and nothing else. So, we turned to a trusted old friend: Google Spreadsheets. A custom Google Spreadsheet was made (the template is publicly available here). It features a scoreboard, so labelers get credit for their contribution, and a mechanism to have at least two people label each tweet to ensure the quality of labels.

 

To ensure the quality of our labels, we decided we need at least two labels on every tweet, and if they are not the same, a third label would be required to break the tie. Row color-coding makes it easy to see which rows are finished. If the row has been labeled once, it will be colored green. If the row has been labeled twice and the two labels do not agree, it will be colored red. Also on the scoreboard page, is a count of how many tweets are labeled once, labeled twice with conflicting labels, and finished on each page.

Step 2: Sentiment analysis (with probability value) of tweets being violent

The sentiment analysis team built a machine learning model to predict whether the tweets are threatening or non-threatening. But first, we needed to address the challenges of an imbalanced dataset where over 90% of the tweet feed was non-threatening, and the scarcity small size of the labeled dataset. We tested multiple techniques, including loss functions specifically designed for imbalanced datasets, undersampling, transfer learning from existing word embeddings algorithms, and ensemble models. We then combined the reservoir of violent signal words to come up with a probability value (the probability that a tweet is more prone to using violent words) against each tweet.

Step 3: Detect influential members in the Twitter gang network

Next, we wanted to identify the influential members of the network. A network analysis resulted in a directed graph and by using the Girvan Newmann algorithm, the communities in the networks could be also detected. Using PageRank values of each node, the influential members were identified.

5 steps to build an effective network analysis of tweets

1. Using python’s networkX, a graph using the mentions and authors of the tweets was created

Network Analysis Gang Violence

Network analysis


Make sure to read this detailed article on network analysis.

The nodes represent mentions in the tweet/author of a tweet. Edge A →B means B was mentioned in the tweet posted by A.

2. Thousands of tweets were used to create a directed graph and using Girvan Newmann algorithm, the communities in the networks were detected. Also, using PageRank values of each node, the influential members in the network could be identified. This value is not crucial to the network analysis but can be useful if one tries to track any gang member who is influential in the network.

3. The members in the communities are either authors or mentions. So, the tweets were then tagged with the community number based on the mention or author names.

4. The total number of signal keywords in all the communities was calculated and so was the total number of signal words for individual communities.

5. The final result was a dataset of tweets that had the community tag and probability of using violent words — based on usage of signal words within the community relative to all the communities. For example, In the picture below, members from Community 1 who are authors or mentions in the tweets are more likely to be inclined towards using violent keywords. So, the tweets which contain authors/mentions from this community are contextually more violent.

Also, the network analysis can give an insight into which members are more influential within the community. One can get a notion by looking at the PageRank values of the members of the community. The greater the PageRank, the more influential a member is.

Page Rank vs Gang Member


Part 2: Correlation between actual violence and tweets

Next, we wanted to understand, if there is any co-relation between actual Crimes and mentions of ‘Gun’ in a threatening tweet.

Below is the correlation between the two metrics on the same day, 1-day, and 2-day shift.

Same day


1-day shift

2-day shift


Through this analysis, we can see that there is a correlation between the number of crimes and the use of a gun in threatening tweets within a 2-day shift. This can be very useful for authorities to prevent gang violence.

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url_link_iconoff
image_sizefull
true_false_conditionoff
true_false_condition_css_selector.et_pb_button
true_false_text_trueTrue
true_false_text_falseFalse
is_audiooff
is_videooff
video_loopon
video_autoplayon
is_oembed_videooff
defer_videooff
defer_video_iconI||divi||400
video_icon_font_sizeoff
pretify_textoff
pretify_seperator,
number_decimal.
show_value_if_zerooff
text_imageoff
is_options_pageoff
is_repeater_loop_layoutoff
linked_post_stylecustom
link_post_seperator,
link_to_post_objecton
loop_layoutnone
columns4
columns_tablet2
columns_mobile1
repeater_dyn_btn_acfnone
text_before_positionsame_line
label_positionsame_line
vertical_alignmentmiddle
admin_labelName
_builder_version4.21.0
_module_presetdefault
title_css_text_alignleft
title_css_font_size14px
title_css_letter_spacing0px
title_css_line_height1em
acf_label_css_text_alignleft
acf_label_css_font_size14px
acf_label_css_letter_spacing0px
acf_label_css_line_height1em
label_css_fontRoboto|700|||||||
label_css_text_alignleft
label_css_letter_spacing0px
text_before_css_font_size14px
text_before_css_letter_spacing0px
text_before_css_line_height1em
seperator_font_size14px
seperator_letter_spacing0px
seperator_line_height1em
relational_field_item_font_size14px
relational_field_item_letter_spacing0px
relational_field_item_line_height1em
background_enable_coloron
use_background_color_gradientoff
background_color_gradient_repeatoff
background_color_gradient_typelinear
background_color_gradient_direction180deg
background_color_gradient_direction_radialcenter
background_color_gradient_stops#2b87da 0%|#29c4a9 100%
background_color_gradient_unit%
background_color_gradient_overlays_imageoff
background_color_gradient_start#2b87da
background_color_gradient_start_position0%
background_color_gradient_end#29c4a9
background_color_gradient_end_position100%
background_enable_imageon
parallaxoff
parallax_methodon
background_sizecover
background_image_widthauto
background_image_heightauto
background_positioncenter
background_horizontal_offset0
background_vertical_offset0
background_repeatno-repeat
background_blendnormal
background_enable_video_mp4on
background_enable_video_webmon
allow_player_pauseoff
background_video_pause_outside_viewporton
background_enable_pattern_styleoff
background_pattern_stylepolka-dots
background_pattern_colorrgba(0,0,0,0.2)
background_pattern_sizeinitial
background_pattern_widthauto
background_pattern_heightauto
background_pattern_repeat_origintop_left
background_pattern_horizontal_offset0
background_pattern_vertical_offset0
background_pattern_repeatrepeat
background_pattern_blend_modenormal
background_enable_mask_styleoff
background_mask_stylelayer-blob
background_mask_color#ffffff
background_mask_aspect_ratiolandscape
background_mask_sizestretch
background_mask_widthauto
background_mask_heightauto
background_mask_positioncenter
background_mask_horizontal_offset0
background_mask_vertical_offset0
background_mask_blend_modenormal
custom_buttonoff
button_text_size20
button_bg_use_color_gradientoff
button_bg_color_gradient_repeatoff
button_bg_color_gradient_typelinear
button_bg_color_gradient_direction180deg
button_bg_color_gradient_direction_radialcenter
button_bg_color_gradient_stops#2b87da 0%|#29c4a9 100%
button_bg_color_gradient_unit%
button_bg_color_gradient_overlays_imageoff
button_bg_color_gradient_start#2b87da
button_bg_color_gradient_start_position0%
button_bg_color_gradient_end#29c4a9
button_bg_color_gradient_end_position100%
button_bg_enable_imageon
button_bg_parallaxoff
button_bg_parallax_methodon
button_bg_sizecover
button_bg_image_widthauto
button_bg_image_heightauto
button_bg_positioncenter
button_bg_horizontal_offset0
button_bg_vertical_offset0
button_bg_repeatno-repeat
button_bg_blendnormal
button_bg_enable_video_mp4on
button_bg_enable_video_webmon
button_bg_allow_player_pauseoff
button_bg_video_pause_outside_viewporton
button_use_iconon
button_icon_placementright
button_on_hoveron
positioningnone
position_origin_atop_left
position_origin_ftop_left
position_origin_rtop_left
text_orientationleft
widthauto
max_widthnone
module_alignmentleft
min_heightauto
heightauto
max_heightnone
custom_margin_tablet||10px||false|false
custom_margin_phone||10px||false|false
custom_margin_last_editedon|tablet
custom_padding5px||||false|false
filter_hue_rotate0deg
filter_saturate100%
filter_brightness100%
filter_contrast100%
filter_invert0%
filter_sepia0%
filter_opacity100%
filter_blur0px
mix_blend_modenormal
animation_stylenone
animation_directioncenter
animation_duration1000ms
animation_delay0ms
animation_intensity_slide50%
animation_intensity_zoom50%
animation_intensity_flip50%
animation_intensity_fold50%
animation_intensity_roll50%
animation_starting_opacity0%
animation_speed_curveease-in-out
animation_repeatonce
hover_transition_duration300ms
hover_transition_delay0ms
hover_transition_speed_curveease
link_option_url_new_windowoff
sticky_positionnone
sticky_offset_top0px
sticky_offset_bottom0px
sticky_limit_topnone
sticky_limit_bottomnone
sticky_offset_surroundingon
sticky_transitionon
motion_trigger_startmiddle
hover_enabled0
title_css_text_align_tabletcenter
title_css_text_align_phonecenter
title_css_text_align_last_editedon|phone
acf_label_css_text_align_tabletcenter
acf_label_css_text_align_phonecenter
acf_label_css_text_align_last_editedon|phone
label_css_text_align_tabletcenter
label_css_text_align_phonecenter
label_css_text_align_last_editedon|desktop
text_orientation_tabletcenter
text_orientation_phonecenter
text_orientation_last_editedon|phone
module_alignment_tabletcenter
module_alignment_phonecenter
module_alignment_last_editedon|desktop
title_css_text_shadow_stylenone
title_css_text_shadow_horizontal_length0em
title_css_text_shadow_vertical_length0em
title_css_text_shadow_blur_strength0em
title_css_text_shadow_colorrgba(0,0,0,0.4)
acf_label_css_text_shadow_stylenone
acf_label_css_text_shadow_horizontal_length0em
acf_label_css_text_shadow_vertical_length0em
acf_label_css_text_shadow_blur_strength0em
acf_label_css_text_shadow_colorrgba(0,0,0,0.4)
label_css_text_shadow_stylenone
label_css_text_shadow_horizontal_length0em
label_css_text_shadow_vertical_length0em
label_css_text_shadow_blur_strength0em
label_css_text_shadow_colorrgba(0,0,0,0.4)
text_before_css_text_shadow_stylenone
text_before_css_text_shadow_horizontal_length0em
text_before_css_text_shadow_vertical_length0em
text_before_css_text_shadow_blur_strength0em
text_before_css_text_shadow_colorrgba(0,0,0,0.4)
seperator_text_shadow_stylenone
seperator_text_shadow_horizontal_length0em
seperator_text_shadow_vertical_length0em
seperator_text_shadow_blur_strength0em
seperator_text_shadow_colorrgba(0,0,0,0.4)
relational_field_item_text_shadow_stylenone
relational_field_item_text_shadow_horizontal_length0em
relational_field_item_text_shadow_vertical_length0em
relational_field_item_text_shadow_blur_strength0em
relational_field_item_text_shadow_colorrgba(0,0,0,0.4)
button_text_shadow_stylenone
button_text_shadow_horizontal_length0em
button_text_shadow_vertical_length0em
button_text_shadow_blur_strength0em
button_text_shadow_colorrgba(0,0,0,0.4)
box_shadow_stylenone
box_shadow_colorrgba(0,0,0,0.3)
box_shadow_positionouter
box_shadow_style_buttonnone
box_shadow_color_buttonrgba(0,0,0,0.3)
box_shadow_position_buttonouter
text_shadow_stylenone
text_shadow_horizontal_length0em
text_shadow_vertical_length0em
text_shadow_blur_strength0em
text_shadow_colorrgba(0,0,0,0.4)
disabledoff
global_colors_info{}

Omdena

Execution time: 0.0010 seconds

Execution time: 0.0003 seconds

Execution time: 0.0005 seconds

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