Internet Safety for Children: Using NLP to Predict the Risk Level of Online Games, Websites, and Applications

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Internet Safety for Children: Using NLP to Predict the Risk Level of Online Games, Websites, and Applications

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value<p>The following work is part of the <a class="c-link" href="https://omdena.com/projects/children-violence/" target="_blank" rel="noopener noreferrer" data-stringify-link="https://omdena.com/projects/children-violence/" data-sk="tooltip_parent">Omdena AI Challenge</a> on improving internet safety for children, implemented in collaboration with <a class="c-link" href="https://www.linkedin.com/in/jzoltner/" target="_blank" rel="noopener noreferrer" data-stringify-link="https://www.linkedin.com/in/jzoltner/" data-sk="tooltip_parent">John Zoltner</a> at <a class="c-link" href="https://www.savethechildren.org/" target="_blank" rel="noopener noreferrer" data-stringify-link="https://www.savethechildren.org/" data-sk="tooltip_parent">Save the Children US</a>.</p>
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The following work is part of the Omdena AI Challenge on improving internet safety for children, implemented in collaboration with John Zoltner at Save the Children US.

Execution time: 0.0019 seconds

Execution time: 0.0004 seconds

Written by Sabrina Carlson and co-authored by Erum Afzal. Contributors include Anna Kolbasko, Juber Rahman, Erum Afzal, Mateus Broilo, Rahul Gopan, Rubens Carvalho, Vinod Rangayyan, Adele C, and Rosana de Oliveira Gomes.

The Problem

Save the Children is a humanitarian organization that aims to improve the lives of children across the globe. In line with the United Nations’ Sustainable Goal 16.2 to “end abuse, exploitation, trafficking, and all forms of violence and torture against children,” Save the Children and Omdena collaborated to use artificial intelligence to identify and prevent online internet violence against children for their safety. Utilizing numerous data sources and a combination of various artificial intelligence techniques, such as natural language processing (NLP), this project’s collaborators aimed to produce meaningful insights into and prevent online internet violence against children for their safety. One area of concern is online games, websites, and applications that are popular with children, and a number of collaborators targeted this space in hopes of guarding children against online predators in the future.

What We Did

The Common Sense Media website provides expert advice, useful tools, and objective ratings for countless movies, television shows, games, websites, and applications to help parents make informed decisions about which content they want their children to consume. Particularly useful for this project, parents, and children can review games, applications, and websites on the Common Sense Media site. A number of Omdena collaborators had the idea to build web scrapers to collect parent and child reviews of the games, applications, and websites that are popular with children and use natural language processing to identify which platforms are high risk for online internet violence against children for their safety.

The first step was to scrape Common Sense Media to collect game, application, and website reviews from both parents and children. To do so, we used ParseHub software to build web scrapers to collect reviews from this website. ParseHub is a powerful, user-friendly tool that allows one to easily extract data from websites. Using ParseHub, we set three different configurations to scrap parent and child reviews of all games, applications, and websites from the internet that Common Sense Media has determined to be popular among children for their safety.

The resulting dataset includes the following features:

  • 40,433 observations (reviews) from 995 different games/apps/websites
  • Platform type (game, application, website)
  • The risk level for online (sexual) violence against children
  • Indicators for each platform’s content related to positive messages, positive role models/representations, ease of play, violence, sex, language, and consumerism. Common Sense Media provides objective ratings (from a scale of 0–5) for these indicators for the digital content included on the site. We focused on the sex indicator and re-labeled it as CSAM (child sexual abuse material). We determined a platform to be high risk for CSAM if its sex rating was greater than 2 and assigned a platform a low-risk CSAM label if its sex rating was lower than 2.

Figure 1 plots the top 20 platforms in terms of the number of reviews.

Internet Safety Children

Figure 1. 20 Most Popular Platforms by Number of Reviews / Source: Omdena


Figure 2 displays the number of reviews for high and low-risk games, applications, and websites. As illustrated in the figure, there are nearly 25,000 reviews for low-risk platforms, whereas there are close to 16,000 reviews for high-risk platforms.

Internet Safety Children

Figure 2. Number of Reviews by CSAM Risk Level / Source: Omdena


Data Sampling

We randomly sampled 50% of the data in order to process the data in a more efficient way. The following graphic illustrates the code used to sample 50% of the data.

Internet Safety Children

Data Cleaning

It is necessary to clean the data in order to build a successful NLP model. To clean the review messages, we created a function called “clean_text” and used it to perform several transformations, including the following:

  • Converted the review text into all lower-case letters
  • Tokenizing the review text (i.e., splitting the text into words) and removing punctuation marks
  • Removing numbers and stopwords (e.g., a, an, the, this).
  • Using the WordNet lexical database to assign Part-Of-Speech (POS) tags. The POS tags are used to attach labels to words that correspond to a noun, verb, etc.
  • Lemmatizing and transforming the words to their roots (i.e., games→ game, Played→ play)

Figure 3 provides an example of the reviews pre-and post-cleaning. In the “review” column, the text has not been cleaned, while the “review_clean” column includes text that has been lemmatized, tagged for POS, tokenized, etc.

Internet Safety Children

Figure 3. Sample of Cleaned Text / Source: Omdena


Feature Engineering

Before applying the models, we performed some feature engineering, including sentiment analysis, vector extraction, and TF-IDF.

Sentiment Analysis

The first feature engineering step was conducting sentiment analysis. The sentiment analysis was performed on the features to gain insight into how parents and children feel about hundreds of games, applications, and internet websites that are popular with children for their safety. We used Vader, which is part of the NLTK module, for the sentiment analysis. Vader uses a lexicon of words to identify positive or negative sentiments in long sentences. It also takes into account the context of the sentences to determine the sentiment scores. For each text, Vader returns the following four values:

  • Negative count score
  • Positive count score
  • Neutral count score
  • The compound — an overall score that summarizes the previous scores

Figure 4 displays a sample of cleaned reviews containing negative, neutral, positive, and compound scores.

Internet Safety Children

Figure 4. Sample of Sentiment Analysis Scores / Source: Omdena


Extracting Vectors

In the next step, we extracted vector representations for every review. Using the module Gensim, we were able to create a numerical vector representation for every word in the corpus using the contexts in which they appear (Word2Vec). This is performed using shallow neural networks. Extracting vectors in this way is interesting and informative because similar words will have similar representation vectors.

All text can also be transformed into numerical vectors using word vectors (Doc2Vec). We can use these vectors as training features because the same texts will also have similar representations.

It was first necessary to train a Doc2Vec model by feeding in our text data. By applying this model to the review text, we are able to obtain the representation vectors. Finally, we added the TF-IDF (Term Frequency — Inverse Document Frequency) values for every word and every document.

But why not simply count the number of times each word appears in every document? The problem with this approach is that it does not take into account the relative importance of the words in the text. For instance, a word that appears in nearly every review would not likely bring useful information for analysis. In contrast, rare words may be much more meaningful. The TF-IDF metric solves this problem:

TF-IDF

The Term Frequency (TF) computes the classic number of times the word appears in the text, while the Inverse Document Frequency (IDF) computes the relative importance of the word depending on the number of texts (reviews) in which the specific word is found. We added TF-IDF columns for every word that appeared in at least 10 different texts. This step allowed us to filter a number of words and, subsequently, reduce the size of the final output. Figure 5 provides the code used to apply TF-IDF and assign the resulting columns to the data frame, and Figure 6 displays the output of the sample code.

Internet Safety Children

Figure 5. TF-IDF Code Sample / Source: Omdena


Internet Safety Children

Figure 6. TF-IDF Sample Code Output / Source: Omdena


Exploratory Data Analysis

The EDA produced a number of interesting insights. Figure 7 provides a sample of reviews that received high negative sentiment scores, and Figure 8 displays a sample of reviews with high positive sentiment scores. The sentiment analysis successfully assigned negative sentiments to reviews with text such as “violence, horror, dead.” The analysis also effectively assigned positive sentiments to reviews containing words such as “fun, cute, exciting.”

Internet Safety Children

Figure 7. Sample of Reviews with High Negative Scores / Source: Omdena


Internet Safety Children

Figure 8. Sample of Reviews with High Positive Scores / Source: Omdena


Figure 9 shows the distribution of the trend of messages among high and low-risk games. Varder categorizes low-risk reviews as positive messages, whereas high-risk reviews should have lower compound sentiments. This shows that the sentiment feature extractions proved helpful in modeling the risk analysis.

Internet Safety Children

Figure 9. High_Low Risk Distribution over Compound Sentiments / Source: Omdena


Modeling High-Risk Games/Applications/Websites

After we successfully scraped the reviews, built the dataset, cleaned the data, and performed feature engineering, we were able to build an NLP model. We choose which features (reviews and clean reviews) to use to train our model.

Then, we split our data into two parts:

  • Training set for training purposes
  • The test set to assess the model performance

After selecting the features and splitting the data into test/training sets, we fit a Random Forest classification model and used the reviews to predict whether a platform is a high risk for CSAM. Figure 10 displays the code used to fit the Random Forest classifier and obtain the metrics.

Internet Safety Children

Figure 10. Random Forest Classifier Code Sample / Source: Omdena


Figure 11 displays a sample of features and their respective importance. The most important features are indeed the ones that were obtained in the sentiment analysis. In addition, the vector representations of the texts were also important in our training. A number of words appear to be fairly important as well.

Figure 11. Feature Importance / Source: Omdena


The Receiver Operating Characteristic Example (ROC) curve and Area Under the Curve (AUC) allow one to evaluate how well a model performs in terms of its ability to distinguish between classes (high/low risk for CSAM in this context). The ROC curve, which plots the true positive rate against the false-positive rate, is displayed in Figure 12. The AUC is 0.77, which indicates that the classifier performed at an acceptable level.

Internet Safety Children

Figure 12. ROC Curve / Source: Omdena


The Precision-Recall (PR) Curve is illustrated in Figure 13. The PR curve is graphed by simply plotting the recall score (x-axis) against the precision score (y-axis). Ideally, we would achieve both a high recall score and a high precision score; however, there is often a trade-off between the two in machine learning. The sci-kit learn documentation states that the Average Precision (AP) “summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight.” The AP here is 0.72, which is an acceptable score.

Internet Safety Children

Figure 13. Precision-Recall Curve / Source: Omdena


It is evident in Figure 13 that the precision decreases as we increase the recall. This indicates that we have to choose a prediction threshold based on our specific needs. For instance, if the end goal is to have a high recall, we should set a low prediction threshold that will allow us to detect most of the observations of the positive class, though the precision will be low. On the contrary, if we want to be really confident about our predictions and are not set on identifying all the positive observations, we should set a high threshold that will allow us to obtain high precision and a low recall.

In order to determine whether or not the model we built performs better than another classifier, we can simply use the AP metric. To assess the quality of our model, we can compare it to a simple decision baseline. With a random classifier for the baseline, the model would simply assign 0 half the time and 1 the other half of the time. Our AP metric is 0.77, which is better than a random classifier.

Conclusion and Observations

It is nearly possible to use just raw text as input to make predictions. The most important aspect is to be able to extract the relevant features from a raw data source. Such data can often complement data science projects, allowing one to extract more meaningful/useful features and increase the model’s predictive power.

We were only able to predict the platform’s risk through user reviews, and it is possible that the reviews are biased. To improve the precision of our predictive model, we can triangulate other features such as player sentiments, game titles, UX/UI features, and in-game chats. Used in combination, these features can provide a number of insightful recommendations. Our predictive model will shed light on CSAM risk in online games, applications, and websites that are popular with children by automatically detecting each platform’s risk level. In the future, we hope that parents will be able to better select platforms for their child’s use based on our use of AI.

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add_css_classoff
add_css_loop_layoutoff
add_css_class_selectorbody
link_new_tabon
link_name_acfoff
link_name_acf_namenone
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
button_alignmentcenter
text_before_positionsame_line
label_positionsame_line
vertical_alignmentmiddle
image_max_width_last_editedon|phone
admin_labelPhoto
_builder_version4.16
_module_presetdefault
title_css_font_size14px
title_css_letter_spacing0px
title_css_line_height1em
acf_label_css_font_size14px
acf_label_css_letter_spacing0px
acf_label_css_line_height1em
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
width100%
max_widthnone
max_width_tablet25%
max_width_phone25%
max_width_last_editedon|tablet
module_alignmentcenter
min_heightauto
heightauto
max_heightnone
custom_margin_tablet||0px||false|false
custom_margin_phone||0px||false|false
custom_margin_last_editedon|phone
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_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)
border_radiion|100%|100%|100%|100%
border_radii_tableton||||
border_radii_phoneon|100%|100%|100%|100%
border_radii_last_editedon|phone
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{}
Favicon

Execution time: 0.0045 seconds

ACF

ID58156
keyfield_623341caec7cf
labelName
nameblog_author_name
prefixacf
typetext
valueOmdena
parent58155
wrapperArray ( [width] => [class] => [id] => )
_nameblog_author_name
_valid1

Module Settings

custom_identifierACF Item
acf_namefield_623341caec7cf
is_author_acf_fieldoff
post_object_acf_namenone
author_field_typeauthor_post
linked_user_acf_namenone
type_taxonomy_acf_namenone
acf_tagp
show_labeloff
label_seperator:
visibilityon
empty_value_optionhide_module
use_iconoff
icon_color#7EBEC5
use_circleoff
circle_color#7EBEC5
use_circle_borderoff
circle_border_color#7EBEC5
use_icon_font_sizeoff
icon_image_placementleft
image_mobile_stackinginitial
return_formatarray
image_link_urloff
image_link_url_acf_namenone
checkbox_stylearray
checkbox_radio_returnlabel
checkbox_radio_value_typeoff
checkbox_radio_linkoff
link_buttonoff
email_subjectnone
email_body_afternone
add_css_classoff
add_css_loop_layoutoff
add_css_class_selectorbody
link_new_tabon
link_name_acfoff
link_name_acf_namenone
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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