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

Date

2020-07-01

Author

Çetinkaya, Yusuf Mucahit
Toroslu, İsmail Hakkı

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

57
views

0
downloads

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 becomes critical in order to contribute positively. In this work, we aim to train a chatbot system that classifies tweets according to their positions, and it can also generate tweets related to a conversation. In this study, we test our system on a recently popular topic, namely the gun control debate in the U.S. Chatbots, are trained to tweet independently for their side and also reply meaningfully to a tweet from the opposite side. State-of-the-art architectures are tested to obtain a more accurate classification. We applied GloVe embedding model for representing tweets. Instead of using handcrafted features, long short-term memory (LSTM) neural network is applied to these embeddings to get more informative and equal-sized feature vectors. This model is trained to encode a tweet as a sequence of embeddings. Encoding is used for both message classification and generation tasks. LSTM sequence-to-sequence model is used to generate topical tweets and replies to tweets. We develop a new salience metric for measuring the relatedness of a generated message to a target tweet. Additionally, human evaluations are performed to measure the quality of the chatbot generated tweets according to their topic relevance and bias, and the quality of its replies to target tweets.

Subject Keywords

Media Technology, Human-Computer Interaction, Communication, Information Systems, Computer Science Applications

URI

https://hdl.handle.net/11511/47529

Journal

SOCIAL NETWORK ANALYSIS AND MINING

DOI

https://doi.org/10.1007/s13278-020-00665-4

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Developing a twitter bot that can join a discussion using state-of-the-art architectures Çetinkaya, Yusuf Mücahit; Toroslu, İsmail Hakkı; Department of Computer Engineering (2019) 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 t...
Combining topology-based & content-based analysis for followee recommendation on Twitter Yanar, Aysu; Karagöz, Pınar; Taşkaya Temizel, Tuğba; Department of Information Systems (2015) Twitter has become an important social platform for individuals and people share a high number of information about their personal lives, interests and viral news during emergencies. As of 2014, Twitter has 240 million active users and approximately 500 million tweets are shared every day. This information overload in Twitter has become a serious problem due to the growing volume of messages and increasing number of users. Recommender systems help to overcome this challenge. Finding interesting users and ge...
TOPONYM RECOGNITION ON TURKISH TWEETS Onal, Kezban Dilek; Karagöz, Pınar; Çakıcı, Ruket (2014-04-25) In recent years, Twitter has become a popular platform for following and spreading trends, news and ideas all over the world. Geographical scope of tweets is crucial to many tasks like disaster management, event tracking and information retrieval. First step for assigning a geographical location to a tweet is toponym recognition. Toponym Recognition (Geoparsing) is identification of toponyms (place names) in a text. In this study, we investigated performance of three existing approaches for toponym recognit...
Determining user types from twitter account contentand structure Gürlek, Mesut; Toroslu, İsmail Hakkı; Department of Computer Engineering (2021-3-05) 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...
A novel pre-processing workflow for popularity prediction in social media Yıldırım, Hüseyin Buğra; Taşkaya Temizel, Tuğba; Department of Information Systems (2021-9-10) 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 and İ. H. Toroslu, “Developing a Twitter bot that can join a discussion using state-of-the-art architectures,” SOCIAL NETWORK ANALYSIS AND MINING, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47529.