Predicting Elections from Social Networks Based on Sub-event Detection and Sentiment Analysis

Unankard, Sayan; Li, Xue; Sharaf, Mohamed A.; Jiang, Zhong Ping; Li, Xueming

doi:10.1007/978-3-319-11746-1_1

Cited by 48 publications

(31 citation statements)

References 12 publications

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“…Foster used 2012 Irish election for sentimental analysis to judge tweets attitude in the election. 29 The effectiveness of the sentiment analysis-based methods was demonstrated by the later usage on the prediction of national election results. 27 Some other works can be found to observe the candidates daily rate in the election such as H. Wang and D. Can's method.…”

Section: Sentimental Analysis Based Methodsmentioning

confidence: 99%

Every word is valuable: Studied influence of negative words that spread during election period in social media

et al. 2018

Concurrency and Computation

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Studying the influence of negative words that spread during election period is an important work in social media. Most of current methods rely on sentiment analysis of tweets to determine the users' preference. However, sentiment analysis can only makes use of emotional words (ie, adverbs and adjectives), which only take 30 percent of the context in the Internet. According to our empirical analysis based on real datasets, the bias on word selection largely reduced the accuracy of the context in the Internet. In order to address this critical problem, we propose a new method that makes use of nouns with emotional context to determine the election preference of each user. By collecting the frequencies of words in context, we weigh the impact of each supportive/objective noun to strengthen the determination of users' preference. Final results will further be integrated to examine the effectiveness and efficiency of our proposed method. To indicate this idea, we collect and adopt real datasets (UK Prime Minister 2017 and US President Campaign 2016) in the experiments. All the experiment results suggested that our integrated method largely outperformed previous prediction methods. In particular, the prediction results were quite similar to the final results of the UK and US election. Meanwhile, for UK election, we found that the daily approval rate is closely related to the event happened everyday. KEYWORDS negative information influence, social media 1 Concurrency Computat Pract Exper. 2019;31:e4525.wileyonlinelibrary.com/journal/cpe

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Section: Sentimental Analysis Based Methodsmentioning

confidence: 99%

Every word is valuable: Studied influence of negative words that spread during election period in social media

et al. 2018

Concurrency and Computation

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“…In [15], earthquake-related tweets are classified. In [19] tweets related to two candidates are collected. In [9], only tweets related to the bombing incident are selected.…”

Section: Introductionmentioning

confidence: 99%

“…Existing work in this field usually requires selecting small portions of data from a large, mixed-domain data body. For example, as a service such as Twitter allows public access to all its data 6 , certain portions of this data have been collected for applications in narrow domains, including earthquake monitoring [15], influenza surveillance [2], election result prediction [18,19], ideal point estimation [1], and rumor detection [5,9].…”

Section: Introductionmentioning

confidence: 99%

Identifying Domains and Concepts in Short Texts via Partial Taxonomy and Unlabeled Data

Zhang

Szabo

Sheng

et al. 2017

Advanced Information Systems Engineering

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Abstract. Accurate and real-time identification of domains and concepts discussed in microblogging texts is crucial for many important applications such as earthquake monitoring, influenza surveillance and disaster management. Existing techniques such as machine learning and keyword generation are application specific and require significant amount of training in order to achieve high accuracy. In this paper, we propose to use a multiple domain taxonomy (MDT) to capture general user knowledge. We formally define the problems of domain classification and concept tagging. Using the MDT, we devise domain-independent pure frequency count methods that do not require any training data nor annotations and that are not sensitive to misspellings or shortened word forms. Our extensive experimental analysis on real Twitter data shows that both methods have significantly better identification accuracy with low runtime than existing methods for large datasets.

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“…This research [109] was published in the proceedings of the 15th International Conference on Web Information System Engineering (WISE) 2014.…”

Section: Sub-event Detection and Sentiment Analysis In Social Networkmentioning

confidence: 99%

“…For a given invariant event, the size of Timecloud indicates the frequency of words over the selected time period as show in Figure 5.5. The color represents the sentiment analysis of the words [109], in which the more darker color the word is, the more positive attitude that the related messages are of. Figure 5.5(a) shows the most discussed words and the messages' attitudes Appendix…”

Section: > Min Matchmentioning

confidence: 99%

Event detection in social networks

Unankard¹

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Micro-blog services such as Twitter generate a large amount of messages carrying event information and users' opinions over a wide range of topics. The events discussed on social networks can be associated with topics, locations, and time periods. The events can be a variety, such as celebrities or political affairs, local social events, accidents, protests, or natural disasters. Messages are posted by users after they have experienced or witnessed the events happening in the real-world and they want to share their experiences immediately. People also express themselves spontaneously with respect to the social events in their social networks. Alternatively, policy-makers may want to know the feelings of users for a particular event to make informed decisions. With the increasing number of real-world events that are originated and discussed over social networks, event detection and tracking is becoming a compelling research issue. However, the traditional approaches to event detection and event tracking on large text streams are not applicable because of the following problems. First, they are not designed to deal with a large number of short and noisy messages. Second, social networks contain network structures such as friends, followers, replies, and re-tweets. Third, social network messages are associated with locations, which can be either senders' current locations or event locations. Fourth, each message is also associated with a timestamp. Messages often contain revealing and timely event information however, traditional text processing approaches assume documents are non-temporal. Moreover, given a particular time frame and a location the user is interested in, events that occurred in the given time frame from the chosen area are more valuable than others. Finding localized events has not been well studied yet.The goals of this thesis are to: (1) identify subsistent problems and challenges in event detection and tracking in streaming micro-blog text, (2) design approaches for event detection and event tracking in social networks, (3) design approaches for sentiment analysis for given event topics, and (4) evaluate the proposed approaches in real-world streaming datasets.In this thesis, our research is considered in three parts. Firstly, in order to detect emerging events from a large number of short and noisy messages, we propose an approach for the early detection of emerging hotspot events in social networks with location sensitivity. An algorithm is designed for slang conversion, synonym expansion and conceptual similarity to provide a rich semantic context for measuring message similarity to improve clustering results. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the hotspot emerging events. A sliding window manager is used to keep track of messages arriving in the system. The size of the sliding window is defined as the time interval. We evaluate our...

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Predicting Elections from Social Networks Based on Sub-event Detection and Sentiment Analysis

Cited by 48 publications

References 12 publications

Every word is valuable: Studied influence of negative words that spread during election period in social media

Every word is valuable: Studied influence of negative words that spread during election period in social media

Identifying Domains and Concepts in Short Texts via Partial Taxonomy and Unlabeled Data

Event detection in social networks

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