Twitter turing test: Identifying social machines

Alarifi, Abdulrahman; Alsaleh, Mansour; Al-Salman, AbdulMalik S.

doi:10.1016/j.ins.2016.08.036

Cited by 78 publications

(44 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This means that Azure Computer Vision is less likely to be affected by background color, and thus is more precise than other computer vision services. 3 ManyChat is a service that allows people to create chatbots for Facebook Messenger https://manychat.com/. 4 Integromat will automate processes that you currently handle manually.…”

Section: Computer Visionmentioning

confidence: 99%

“…The 1 Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. wide utilization of NLP has led to more intellectual and natural interactions through conversations between people and machines in therapy, customer service, virtual assistance, and many other areas [3] [4] [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching

Zhang¹,

Wang²

2019

JCC

View full text Add to dashboard Cite

With the development of artificial intelligence in recent decades, intelligent algorithms such as visual recognition and natural language processing have affected and will continue to affect our lives. A smart device designed to help people make wise decisions on dress through dialogue would be a very convenient tool. By using a color extraction algorithm and natural language processing methods, we have built an outfit planning chatbot, named Outfit Helper, that can help male users decide what to wear in order to dress well. According to our study, most users think that the Outfit Helper is a good choice for solving the clothing matching problem. This study shows that our lives can become much easier by developing and using artificial intelligence to help us choose our clothing.

show abstract

Section: Computer Visionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching

Zhang¹,

Wang²

2019

JCC

View full text Add to dashboard Cite

show abstract

“…Recent surveys of existing spam detection techniques and mechanisms have analyzed their advantages and disadvantages (e.g., [42] and [43]). It should be noted that spam and automated accounts in social networks have also contributed to the prevalence of web spam (e.g., see [44–48]). The detection features used for web spam in previous studies belong to two categories: (1) those that exploit topology and network-related data; and (2) those that exploit the web page content.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers

Alsaleh

Alarifi

2016

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents.

show abstract

“…Organizations that gather and break down information from social media, news and other information streams are confronted with a few difficulties that involve storing and handling of large amount of information. Hence, it is a challenge to gather all the related information from different sources in order to analyze and present it in such a way for non-technical domain experts to understand and self-interpret the valuable significant information [8]. Therefore, a combination between sentiment analysis and interactive visualization are suggested.…”

Section: Introductionmentioning

confidence: 99%

Visualization of Crime News Sentiment in Facebook

Haron¹,

Abidin²,

Zamani³

et al. 2018

IJET

View full text Add to dashboard Cite

Facebook has become a popular platform in communicating information. People can express their opinions using texts, symbols, pictures and emoticons via Facebook posts and comments. These expressions allow sentiment analysis to be performed by collecting the data to obtain the public's opinions and emotions toward certain issues. Due to a huge amount of data obtained from Facebook, proper approaches are required to cater the texts and symbols used in the comments. There are also limited amount of dictionary on Malay texts which make it more challenging to process and classify the positive and negative words used in the comments. Thus, hybrid approach is applied during the data processing to visualize the results. In this work, a combination of lexicon-based approach and Naïve Bayes are used. This study focuses on analyzing the public's sentiments on crime news in Facebook by using word cloud visualization. The visualization displays important words used in a form of a word cloud. Moreover, the percentage of positive and negative words existed in the comments is also shown as part of the visualization results.

show abstract

Twitter turing test: Identifying social machines

Cited by 78 publications

References 13 publications

Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching

Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers

Visualization of Crime News Sentiment in Facebook

Contact Info

Product

Resources

About