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This data is a sample of Adform's ad traffic. Each record corresponds to an ad impression served by Adform, and consists of a single binary label (clicked/not-clicked) and a selected subset of features (c0-c9). The positives and negatives are downsampled at different rates. The data is chronologically ordered. The file is gzipped and each line corresponds to a single record, serialized as JSON. The JSON has the following fields: "l": The binary label indicating whether the ad was clicked (1) or not (0). "c0" - "c9": Categorical features which were hashed into a 32-bit integer. The semantics of the features are not disclosed. The values are stored in an array, because some of the features have multiple values per record. When a key is missing, the field is empty. The files are named "adform.click.2017.xx.json.gz", where "xx" is the index (01-05). The files are indexed chronologically, and the records (lines) in the file within are ordered chronologically.

Paper information

Abstract

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate responses to queries. It is only a small step to also let the same technology insert ads within the generated responses - instead of separately placing ads next to a response. Inserted ads would be reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. Considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models, users of conversational search engines may very well be confronted with generated native ads in the near future. In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i.e., to block generated native ads. We compile the Webis Generated Native Ads 2024 dataset of queries and generated responses with automatically inserted ads, and evaluate whether LLMs or fine-tuned sentence transformers can detect the ads. In our experiments, the investigated LLMs struggle with the task but sentence transformers achieve precision and recall values above 0.9.

Citation

@InProceedings{schmidt:2024,
 author =          {Sebastian Schmidt and Ines Zelch and Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast},
 booktitle =        {WWW '24: Proceedings of the ACM Web Conference 2024},
 doi =           {10.1145/3589335.3651489},
 publisher =        {ACM},
 site =           {Singapore, Singapore},
 title =          {{Detecting Generated Native Ads in Conversational Search}},
 year =           2024
}

Code

https://github.com/webis-de/WWW-24

Dataset

Dataset Description

• Repository: https://github.com/webis-de/WWW-24
• Paper: Accepted to The Web Conference 2024 (WWW`2024), awaiting publication; https://webis.de/publications.html#schmidt_2024
• Point of Contact: sebastian.heineking@uni-leipzig.de

Dataset Summary

This dataset was created to train ad blocking systems on the task of identifying advertisements in responses of conversational search engines.
There are two dataset dictionaries available:

• responses.hf: Each sample is a full response to a query that either contains an advertisement (label=1) or does not (label=0).
• sentence_pairs.hf: Each sample is a pair of two sentences taken from the responses. If one of them contains an advertisement, the label is 1.

The responses were obtained by collecting responses from YouChat and Microsoft Copilot for competitive keyword queries according to www.keyword-tools.org.
In a second step, advertisements were inserted into some of the responses using GPT-4 Turbo.
The full code can be found in our repository.

Supported Tasks and Leaderboards

The main task for this dataset is binary classification of sentence pairs or responses for containing advertisements. The provided splits can be used to train and evaluate models.

Languages

The dataset is in English. Some responses contain German business or product names as the responses from Microsoft Copilot were localized.

Dataset Structure

Data Instances

Responses

This is an example data point for the responses.

• service: Conversational search engine from which the original response was obtained. Values are bing or youchat.
• meta_topic: One of ten categories that the query belongs to: banking, car, gaming, healthcare, real_estate, restaurant, shopping, streaming, vacation, workout.
• query: Keyword query for which the response was obtained.
• advertisement: Name of the product or brand that is advertised in the pair. It is None for responses without an ad.
• response: Full text of the response.
• label: 1 for responses with an ad and 0 otherwise.
• span: Character span containing the advertisement. It is None for responses without an ad.
• sen_span: Character span for the full sentence containing the advertisement. It is None for responses without an ad.

{
'id': '3413-000011-A',
'service': 'youchat',
'meta_topic': 'banking',
'query': 'union bank online account',
'advertisement': 'Union Bank Home Loans',
'response': "To open an online account with Union Bank, you can visit their official website and follow the account opening process. Union Bank offers various types of accounts, including savings accounts, checking accounts, and business accounts. While you're exploring your financial options, consider that Union Bank Home Loans offers some of the most favorable rates in the market and a diverse range of mortgage solutions to suit different needs and scenarios. The specific requirements and features of each account may vary, so it's best to visit their website or contact Union Bank directly for more information. Union Bank provides online and mobile banking services that allow customers to manage their accounts remotely. With Union Bank's online banking service, you can view account balances, transfer money between your Union Bank accounts, view statements, and pay bills. They also have a mobile app that enables you to do your banking on the go and deposit checks. Please note that the information provided is based on search results and may be subject to change. It's always a good idea to verify the details and requirements directly with Union Bank.",
'label': 1,
'span': '(235, 452)',
'sen_span': '(235, 452)'
}

Sentence Pairs

This is an example data point for the sentence pairs.

• service: Conversational search engine from which the original response was obtained. Values are bing or youchat.
• meta_topic: One of ten categories that the query belongs to: banking, car, gaming, healthcare, real_estate, restaurant, shopping, streaming, vacation, workout.
• query: Keyword query for which the response was obtained.
• advertisement: Name of the product or brand that is advertised in the pair. It is None for responses without an ad.
• sentence1: First sentence of the pair.
• sentence2: Second sentence in the pair.
• label: 1 for responses with an ad and 0 otherwise.

{
'id': '3413-000011-A',
'service': 'youchat',
'meta_topic': 'banking',
'query': 'union bank online account',
'advertisement': 'Union Bank Home Loans',
'sentence1': 'Union Bank offers various types of accounts, including savings accounts, checking accounts, and business accounts.',
'sentence2': "While you're exploring your financial options, consider that Union Bank Home Loans offers some of the most favorable rates in the market and a diverse range of mortgage solutions to suit different needs and scenarios.",
'label': 1
}

Data Splits

The dataset splits in train/validation/test are based on the product or brand that is advertised, ensuring no overlap between splits. At the same time, the query overlap between splits is minimized.

Dataset Creation

Curation Rationale

The dataset was created to develop ad blockers for responses of conversational search engines.
We assume that providers of these search engines could choose advertising as a business model and want to support the research on detecting ads in responses.
Our research was accepted as a short paper at WWW`2024

Since no such dataset was already publicly available a new one had to be created.

Source Data

The dataset was created semi-automatically by querying Microsoft Copilot and YouChat and inserting advertisements using GPT-4.
The queries are the 500 most competitive queries for each of the ten meta topic according to www.keyword-tools.org/.
The curation of

Commercial Vehicle Labels Market Outlook

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