Developing a twitter bot that can join a discussion using state-of-the-art architectures

Çetinkaya, Yusuf Mücahit
Twitter is today mostly used for sharing and commenting about news. In this manner, the interaction between Twitter users is inevitable. This interaction sometimes causes people to move daily debates to this social platform. Since being dominant in these debates is crucial, automation of this process becomes highly popular. In this work, we aim to train a bot that classifies posted tweets according to their semantic and generates logical tweets about a popular discussion, namely gun debate of the U.S. for this study. Bots are trained to tweet independently on their side of the debate and also reply to a tweet from opposite view. State-of-art architectures are tested to get more accurate classification. We have applied GloVe embedding model for representing tweets. Instead of using handcrafted features, long-short-term memory neural network is applied to these embeddings to get more informative and equal size feature vectors. This model is trained to encode the tweet by fed as a sequence of embeddings. Encoding is used for both classification and generation tasks. LSTM sequence to sequence model is used to generate tweets and replies to tweets. The attention mechanism is added to the reply model to produce more related tweets. We propose a new metric for measuring the relatedness of the reply to the target tweet. Additionally, human evaluators measure the quality of generated tweets according to relatedness to the topic and target tweet, which is replied.


Developing a Twitter bot that can join a discussion using state-of-the-art architectures
Çetinkaya, Yusuf Mucahit; Toroslu, İsmail Hakkı (Springer Science and Business Media LLC, 2020-07-01)
Today, microblogging platforms like Twitter have become popular by spreading news and opinions that gather attention. Engaging interactions, such as likes, shares, and replies, between users are the key determinants of these platforms' news feed prioritization algorithms. These interactions attract people to ongoing debates and help inform and shape their opinions. Since being influential and attracting followers in these debates are considered as important, understanding the automation of these processes b...
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Karatay, Deniz; Karagöz, Pınar (null; 2015-05-18)
Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. In this work we propose a Named Entity Recognition (NER) based user profile modeling for Twitter users and employ this model to generate personalized tweet recommendations. Effectiveness of the proposed method is shown through a set of experiments. Copyright © 2015 held by author(s).
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People are using social media platforms more and more every day; hence, they are be-coming suitable for research studies by their rich content. Twitter is one of the biggestand most widely used social media platforms, and many studies focus on Twitter forsocial media research. In this thesis, we propose methodologies for determining usertypes of Twitter accounts by their metadata, content, and structure. Our first problemis classifying organization vs. individual account types using only metadata. After weg...
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Social uprisings clearly show that social media tools, especially Twitter, help news spread more than the press does recently. In some cases Twitter substitutes traditional media if censorship is enlarged to such a level that the mainstream media channels prefer not to reflect the actual volume of the protests. Twitter is also utilized by politicians during such events to reinforce "us vs. them" division, and to gain support and legitimization for their own actions. Using critical discourse analysis, this p...
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Users in Twitter are in continuous interaction with each other through posts and reactions such as likes and retweets. Tweets often get a little reaction from people, with only a few of them receiving a prominent response. Thus, reaction numbers result in having a heavy right-skewed distribution. Furthermore, some tweets show unexpected response performance that cannot be depicted by standard features and are often dependent on extraordinary situations such as being the first reporter and mass reaction. Hea...
Citation Formats
Y. M. Çetinkaya, “Developing a twitter bot that can join a discussion using state-of-the-art architectures,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.