Results of the summarizing process for the arXiv paper: 2304.03144v1

The detection of bot accounts in online social networks is a persistent concern for genuine users who are often subjected to harassment and invasion by these malicious entities. To address this issue, we propose a unified content embedding framework called BOTTRINET that utilizes textual content posted by accounts for bot detection. Our approach assumes that contexts naturally reveal account personalities and habits, making the extraction of bot-related information using embedding techniques efficient and valuable. Compared to the side information approach commonly used in bot detection, our content approach model can be deployed in each content-related user action such as posting, commenting, or sending messages. This allows for faster detection without waiting an extended period to collect data. We also introduce the first practice of applying metric learning techniques in the bot detection field, which has never been considered before. Our method makes several contributions to the field of bot detection. Firstly, we apply Triplet Learning method from computer vision to the bot detection field by abstracting an account's entire lifetime as an Anchor and capturing its intrinsic nature as an independent entity of behavior and content. This breakthrough significantly improves bot detection performance. Secondly, we design a simple and easy-to-use embedding framework called BOTTRINET that represents raw embeddings into more efficient account embeddings, greatly enhancing the separability between bot and genuine accounts. We evaluate our system on a real-world dataset CRESCI2017 comprising three bot account categories and five bot sample sets. Our method achieves the highest average accuracy of 98.34% and f1score of 97.99% on two content-intensive bot sets outperforming previous work and becoming state-of-the-art. It also makes a breakthrough on four content-less bot sets with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%. In conclusion, our proposed BOTTRINET framework provides a reliable solution for detecting bots quickly and accurately in online social networks using textual content posted by accounts.

• Bot accounts in online social networks are a persistent concern for genuine users who are often subjected to harassment and invasion by these malicious entities.
• The proposed solution is a unified content embedding framework called BOTTRINET that utilizes textual content posted by accounts for bot detection.
• The approach assumes that contexts naturally reveal account personalities and habits, making the extraction of bot-related information using embedding techniques efficient and valuable.
• Compared to the side information approach commonly used in bot detection, our content approach model can be deployed in each content-related user action such as posting, commenting, or sending messages. This allows for faster detection without waiting an extended period to collect data.
• The method makes several contributions to the field of bot detection including applying Triplet Learning method from computer vision to the bot detection field and designing a simple and easy-to-use embedding framework called BOTTRINET that represents raw embeddings into more efficient account embeddings.
• The system was evaluated on a real-world dataset CRESCI2017 comprising three bot account categories and five bot sample sets. It achieved the highest average accuracy of 98.34% and f1score of 97.99% on two content-intensive bot sets outperforming previous work and becoming state-of-the-art.
• In conclusion, the proposed BOTTRINET framework provides a reliable solution for detecting bots quickly and accurately in online social networks using textual content posted by accounts.

Detecting Bots in Online Social Networks with BOTTRINET

Online social networks have become an increasingly popular platform for people to connect and share information. However, this popularity has also made them a target for malicious bot accounts that can be used to spread misinformation or harass genuine users. To address this issue, researchers from the University of Padova proposed a unified content embedding framework called BOTTRINET that utilizes textual content posted by accounts for bot detection. This approach assumes that contexts naturally reveal account personalities and habits, making the extraction of bot-related information using embedding techniques efficient and valuable.

The Triplet Learning Method

The research team applied the Triplet Learning method from computer vision to the bot detection field by abstracting an account's entire lifetime as an Anchor and capturing its intrinsic nature as an independent entity of behavior and content. This breakthrough significantly improves bot detection performance compared to side information approaches commonly used in bot detection which require waiting extended periods of time to collect data before they can be deployed.

BOTTRINET Framework

The researchers then designed a simple and easy-to-use embedding framework called BOTTRINET that represents raw embeddings into more efficient account embeddings, greatly enhancing the separability between bots and genuine accounts. The system is able to detect bots quickly without waiting extended periods of time because it can be deployed in each content-related user action such as posting, commenting or sending messages.

Evaluation Results

To evaluate their system, the research team tested it on a real world dataset CRESCI2017 comprising three different categories of bots: spamming bots; fake news spreading bots; and opinion manipulation bots with five sample sets: two content intensive sets (Twitter Spambots 1 & 2)and three contentless sets (Twitter Fake Followers 1 & 2; Twitter Political Bots). Their method achieved impressive results outperforming previous work with highest average accuracy at 98.34% and f1score at 97.99%. It also made a breakthrough on four contentless bot sets with an average accuracy improvement of 11.52% and an average f1score increase of 16.70%.

Conclusion

In conclusion, BOTTRINET provides a reliable solution for detecting malicious bots quickly and accurately in online social networks using textual content posted by accounts without relying on side information approaches which often take longer periods of time before they can be deployed effectively..