Matching Items (9)
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- All Subjects: Twitter
- Creators: Davulcu, Hasan
- Member of: Theses and Dissertations
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Corporations invest considerable resources to create, preserve and analyze
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
ContributorsWright, Jeremy (Author) / Syrotiuk, Violet (Thesis advisor) / Davulcu, Hasan (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2014
Description
Twitter has become a very popular social media site that is used daily by many people and organizations. This paper will focus on the financial aspect of Twitter, as a process will be shown to be able to mine data about specific companies' stock prices. This was done by writing a program to grab tweets about the stocks of the thirty companies in the Dow Jones.
ContributorsLarson, Grant Elliott (Author) / Davulcu, Hasan (Thesis director) / Ye, Jieping (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.
ContributorsHarikumar, Swathikrishna (Author) / Davulcu, Hasan (Thesis director) / Bodford, Jessica (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter).
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
ContributorsAwasthi, Piyush (Author) / Davulcu, Hasan (Thesis advisor) / Tong, Hanghang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the advent of social media and micro-blogging sites, people have become active in sharing their thoughts, opinions, ideologies and furthermore enforcing them on others. Users have become the source for the production and dissemination of real time information. The content posted by the users can be used to understand them and track their behavior. Using this content of the user, data analysis can be performed to understand their social ideology and affinity towards Radical and Counter-Radical Movements. During the process of expressing their opinions people use hashtags in their messages in Twitter. These hashtags are a rich source of information in understanding the content based relationship between the online users apart from the existing context based follower and friend relationship.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
An intelligent visual dash-board system is necessary which can track the activities of the users and diffusion of the online social movements, identify the hot-spots in the users' network, show the geographic foot print of the users and to understand the socio-cultural, economic and political drivers for the relationship among different groups of the users.
ContributorsGaripalli, Sravan Kumar (Author) / Davulcu, Hasan (Thesis advisor) / Shakarian, Paulo (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, groups and organization leaders. Finally, the experimental results are presented and a subset of relevant features is identified that lead to a generalizable model. Detection of tiny number of groups from large network is achieved with 0.8 precision, 0.75 recall and 0.77 F1 score. The domain independent network and behavioral features and models developed here are suitable for solving twitter account classification problem in any context.
ContributorsGore, Chinmay Chandrashekhar (Author) / Davulcu, Hasan (Thesis advisor) / Hsiao, Ihan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
Description
The development of the internet provided new means for people to communicate effectively and share their ideas. There has been a decline in the consumption of newspapers and traditional broadcasting media toward online social mediums in recent years. Social media has been introduced as a new way of increasing democratic discussions on political and social matters. Among social media, Twitter is widely used by politicians, government officials, communities, and parties to make announcements and reach their voice to their followers. This greatly increases the acceptance domain of the medium.
The usage of social media during social and political campaigns has been the subject of a lot of social science studies including the Occupy Wall Street movement, The Arab Spring, the United States (US) election, more recently The Brexit campaign. The wide
spread usage of social media in this space and the active participation of people in the discussions on social media made this communication channel a suitable place for spreading propaganda to alter public opinion.
An interesting feature of twitter is the feasibility of which bots can be programmed to operate on this platform. Social media bots are automated agents engineered to emulate the activity of a human being by tweeting some specific content, replying to users, magnifying certain topics by retweeting them. Network on these bots is called botnets and describing the collaboration of connected computers with programs that communicates across multiple devices to perform some task.
In this thesis, I will study how bots can influence the opinion, finding which parameters are playing a role in shrinking or coalescing the communities, and finally logically proving the effectiveness of each of the hypotheses.
The usage of social media during social and political campaigns has been the subject of a lot of social science studies including the Occupy Wall Street movement, The Arab Spring, the United States (US) election, more recently The Brexit campaign. The wide
spread usage of social media in this space and the active participation of people in the discussions on social media made this communication channel a suitable place for spreading propaganda to alter public opinion.
An interesting feature of twitter is the feasibility of which bots can be programmed to operate on this platform. Social media bots are automated agents engineered to emulate the activity of a human being by tweeting some specific content, replying to users, magnifying certain topics by retweeting them. Network on these bots is called botnets and describing the collaboration of connected computers with programs that communicates across multiple devices to perform some task.
In this thesis, I will study how bots can influence the opinion, finding which parameters are playing a role in shrinking or coalescing the communities, and finally logically proving the effectiveness of each of the hypotheses.
ContributorsAhmadi, Mohsen (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020
Description
In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting of news headlines, tweets, and the price of the cryptocurrency. I fed this data into a Long Short-Term Memory Neural Network and built a model that predicted Bitcoin price for a new timeframe. The model correctly predicted 75% of test set price trends on 3.25 hour time intervals. This is higher than the 53.57% accuracy tested with a Bitcoin price model without sentiment data. I concluded public reaction to Bitcoin news headlines has an effect on the short-term price direction of the cryptocurrency. Investors can use my model to help them in their decision-making process when making short-term Bitcoin investment decisions.
ContributorsSteinberg, Sam (Author) / Boscovic, Dragan (Thesis director) / Davulcu, Hasan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05