Jibunu has integrated with Research Defender to provide additional data quality services. Research Defender is a respondent fingerprinting and fraud detection service that can be added to a survey. More information can be found at: https://researchdefender.com/.
Research Defender uses multiple techniques to identify and prevent fraudsters. These techniques range from tracking common fraudulent activities (e.g., TOR usage, location spoofing), to behavioral tracking of professional survey takers. In addition, they use a variety of tools to help identify and root out trends.
Research Defender’s Search tool is intended to be used as a loading screen by default. However, it can also be used as an introduction screen where the respondent sees the introduction survey text. The process is invisible to the user and does not interfere with the user experience. Research Defender is compatible with privacy and data protection laws.
Research Defender is not enabled on any survey unless requested. To have Research Defender enabled on a study, notify the Project Manager. Please note that there are additional costs associated with adding Research Defender to a survey. Contact the Project Manager for more details.
By default, all terminates based on Research Defender are disabled. If you would like to terminate based on any of the Research Defender criteria, please notify the project manager.
Search tool options include:
- Store the data with no other action taken (default)
- Terminate the respondent based on the result:
- threat_potential_score: The recommended logic for terminating based on the threat potential score would be anyone with a 29 or higher.
- duplicate_score: The recommended logic for terminating based on the duplicate score would be anyone with 100.
- If terminating based on the Research Defender response, Jibunu will also terminate the respondent if the API call is not able to complete for any reason.
- Additional custom terminate criteria can be added such as using a combination of factors to terminate a respondent.
In addition, to these data flags, Research Defender has a Review tool that can be added to analyze open-end responses. If this is a tool you would like included in your survey, we need a programming note stating that “/Review” should be added to specified open-end questions.
Research Defender Data Fields and Values
| Data Flag | Description |
| Search Tool | |
| threat_potential | Text description of respondent’s threat level (e.g. known fraudster from existing database). External data providers (EDPs) are utilized to help identify this. This value defaults to “Low”. If only one of the EDPs identifies a respondent as risky, they are flagged as “Medium”. If more than one of the EDPs identifies them as high risk, they are flagged as “High”. |
| threat_potential_score | Numeric value of respondent’s threat level (e.g. known fraudster from existing database). External data providers (EDPs) are utilized to help identify this. Range of values as follows: Low (1 to 33), Medium (34 to 67) and High (68 to 100). |
| respondent_risk | Identifies fraudulent / bad actors (TOR Network, Private VPNs, etc.). External data providers (EDPs) are utilized to help identify this. This value defaults to “0”. If more than one of the EDPs identifies them as high risk, we flag them as “1”. |
| country_code | Text code of country in ISO format (e.g. US, PH, IN) |
| country | Text description of country of respondent (e.g. United States, India) |
| respondent_ud | This identifier helps our clients manage the identity of the respondent. Specifically, it allows clients to implement rules that are more granular – for example, using this identifier, clients can allow re-entry into specific surveys because of some client specific business rule |
| duplicate_potential | Currently, this value is “low” or “high”. Over time, we will use incoming data to further track behavioral metrics to help clients identify a “medium” which will produce a more nuanced understanding of a suspicious duplicate survey attempt |
| duplicate_score | This is the float/numeric equivalent of the “duplicate_potential” field. Currently, this value is either “0” or “100”. Over time, we will use incoming data to further track behavioral metrics to help clients identify a range which will produce a more nuanced understanding of a suspicious duplicate survey attempt. |
| duplicate_initial_ud | In the case of an existing duplicate, this field would return the original sn_ud to help our clients understand which value was originally a duplicate |
| flag | Identifies previous access to the survey; 1 if true and 0 if false |
| failure_reason | Returns a code based on the respondent’s fraudulent status (Duplicate entrant, TOR users etc..). Below are the following code and its description: “02” – Duplicate entrant into survey “03” – User Emulator “04” – Nefarious VPN usage detected “05” – TOR network detected “06” – Public proxy server detected “07” – Web proxy service used “08” – Web crawler usage detected “09” – Internet fraudster detected “10” – Retail and ad-tech fraudster detected “11” – IP Address subnet detected String “12” – Recent Abuse detected “13” – Duplicate Survey Group detected “14” – Navigator Webdriver detected “15” – Developer tool detected “16” – Web RTC IP Address detected (visible with clients that has `threat_potential_score=31` considered to be `threat_potental=medium` |
| country_mismatch | Identifies country mismatch between respondent and stated country. 1 if true and 0 if false. |
| sn_ud | Respondent’s Unique ID |
| survey_number | Jibunu survey number |
| Review Tool (for Open-Ends) | |
| language_detected | Text description of the language used in the response (e.g. English, French) |
| language_detected_score | Confidence interval with which the language is correctly detected |
| garbage_words and garbage_words_score | No longer used. Language = Unknown is a better barometer for highlighting responses containing “garbage words”. |
| profanity_check | Detects whether or not profanity is used in response; 1 if true and 0 if false |
| profanity_check_score | If the response contains profanity, 1. Otherwise 0. We don’t recommend using this parameter. |
| pasted_response | Detects whether the text response was pasted from the user’s clipboard |
| pasted_response_score | Value represents the number of words used from the clipboard in the answer (as a %). For example, in an answer of 10 characters and 4 of the characters were pasted from the respondent’s clipboard, the pasted_response_score will be 0.4 |
| engagement_score | Measures the length of a response compared to other responses within the same Question ID. If a response is the same length, the engagement_score will be equal to 1. If a response is shorter than the average length within the question ID, the engagement_score will be less than 1. If a response is longer than the average length within the question ID, the engagement_score will be greater than 1. |
| composite_score | This parameter takes into account all the other parameters in order to produce an overall score for the response including Copy/Paste detection, Profanity detection, Engagement Score, Language Detection, Etc. |
| page_view_time | Time it takes from page load to submitting the text (in milliseconds). Note – this is a stand-alone parameter and does not factor into the composite score. |
| typed_response_time | Time it takes from the start of entering the text (answer) to submitting the text (in milliseconds). Note – this is a stand-alone parameter and does not factor into the composite score. |
| similarity_text | /REVIEW will use the s_text_length parameter on the API Call which dictates the minimum number of characters needed in the response to perform the check. With an s_text_length=5, the response “cow” would not be checked by Similarity, but “Gorilla” would because it’s more than 5 characters. This is helpful to sniff out bots or scripts that provide identical responses to Open-Ended questions. For example, if there was another response to the same question that matched “going on a cruise just before the pandemic”, it would return a “similarity_text”: 1 indicating a duplicate open end |
Updated 4/09/24
