S.P.A.M. Fighting SPAM
Format
SOECS Senior Project Demonstration
Faculty Mentor Name
Shon Vick
Faculty Mentor Department
Computer Science
Additional Faculty Mentor Name
Osvaldo Jimenez
Additional Faculty Mentor Department
Computer Science
Abstract/Artist Statement
On Twitch.tv, streamers encounter issues where human moderators must continuously monitor live channels to prevent inappropriate discussion. Additionally the streamers are not able to take advantage of the rapid stream of information coming from their viewers. These problems stem from the large amount of data that is difficult for humans to process and are much more suited for a programmatic solution. Our system will allow streamers on Twitch.tv to apply automatic moderation to their streaming channel and will give insights into viewer trends and information. Currently, systems exist to solve similar problems but rely on human interaction to moderate channels or very limited bot interactions and provide only big picture statistical information. Our bot interacts with the Twitch.tv IRC channel, reading user input and server messages to determine previous actions taken against users as good or bad, learn from said actions, and be able to make accurate moderating actions. We are in the process of scrubbing chats and working through IRC logs to be able to train the bot to react properly to our specified criteria. In addition we have a preliminary classifier running that allows us to make judgements based on certain user and chat message statistics. In this paper we will detail the methods used to collect, label, and learn from the information gathered in addition to the methods of providing statistics. Need for human intervention to moderate and parse the constant streams of data that go through Twitch.tv motivated us to automate parts of the process. The kinds of statistics taken from streams, users, and channels allow us to take advantage of machine learning techniques to provide an enhanced experience for all.
Location
School of Engineering & Computer Science
Start Date
4-5-2018 2:30 PM
End Date
4-5-2018 4:00 PM
S.P.A.M. Fighting SPAM
School of Engineering & Computer Science
On Twitch.tv, streamers encounter issues where human moderators must continuously monitor live channels to prevent inappropriate discussion. Additionally the streamers are not able to take advantage of the rapid stream of information coming from their viewers. These problems stem from the large amount of data that is difficult for humans to process and are much more suited for a programmatic solution. Our system will allow streamers on Twitch.tv to apply automatic moderation to their streaming channel and will give insights into viewer trends and information. Currently, systems exist to solve similar problems but rely on human interaction to moderate channels or very limited bot interactions and provide only big picture statistical information. Our bot interacts with the Twitch.tv IRC channel, reading user input and server messages to determine previous actions taken against users as good or bad, learn from said actions, and be able to make accurate moderating actions. We are in the process of scrubbing chats and working through IRC logs to be able to train the bot to react properly to our specified criteria. In addition we have a preliminary classifier running that allows us to make judgements based on certain user and chat message statistics. In this paper we will detail the methods used to collect, label, and learn from the information gathered in addition to the methods of providing statistics. Need for human intervention to moderate and parse the constant streams of data that go through Twitch.tv motivated us to automate parts of the process. The kinds of statistics taken from streams, users, and channels allow us to take advantage of machine learning techniques to provide an enhanced experience for all.