Researchers from the Queensland University of Technology (QUT) in Australia have developed an algorithm that detects misogynistic content on Twitter.

The team developed the system by first mining 1 million tweets. They then refined the dataset by searching the posts for three abusive keywords: whore, slut, and rape.

Next, they categorized the remaining 5,000 tweets as either misogynistic or not, based on their context and intent. These labeled tweets were then fed to a machine learning classifier, which used the samples to create its own classification model.

The system uses a deep learning algorithm to adjust its knowledge of terminology as language evolves. While the AI built up its vocabulary, the researchers monitored the context and intent of the language, to help the algorithm differentiate between abuse, sarcasm, and “friendly use of aggressive terminology.”

“Take the phrase ‘get back to the kitchen’ as an example — devoid of context of structural inequality, a machine’s literal interpretation could miss the misogynistic meaning,” said Professor Richi Naya, a co-author of the study.

“But seen with the understanding of what constitutes abusive or misogynistic language, it can be identified as a misogynistic tweet.”

Nayak said this enabled the system to understand different contexts just by analyzing text, and without the help of tone.

We were very happy when our algorithm identified ‘go back to the kitchen’ as misogynistic — it demonstrated that the context learning works.

The researchers say the model identifies misogynistic tweets with 75% accuracy. It could also be adjusted to spot racism, homophobia, or abuse of disabled people.

The team now wants social media platforms to develop their research into an abuse detection tool.

“At the moment, the onus is on the user to report abuse they receive,” said Naya. “We hope our machine-learning solution can be adopted by social media platforms to automatically identify and report this content to protect women and other user groups online.”

You can read the research paper on the Springer database of academic journals.

The post New algorithm can identify misogyny on Twitter appeared first on Artificial Intelligence.

During the last decade, social networking sites/apps have become the most important channels of communication.Social networks such as Facebook, Twitter, and Instagram contain a considerable amount of informative data not only about social matters but also about business and marketing.

Of course, if you want to take advantage of this ever-changing space to swing the balance in your brand’s favor, you don’t have a choice unless using big data analytics.In this post, I’m going to describe how social media can be affected by big data analytics and how businesses can make the most out of it.

What is big data?

Big data is any novel technique used to analyze a massive volume of data that is so large it is impossible to process with traditional methods.Big data can handle both structured and unstructured data and help you tackle the problem of processing power. The main purpose of using big data techniques is finding overall patterns, trends, and connections between different variables.This is particularly important for studying data related to human interactions and social behaviors. This is exactly where social media can be influenced by big data, especially for marketing purposes.In fact, the huge, unstructured knowledge available on social media can’t add real value to your marketing strategy. So you need a powerful tool like big data analytics to be able to handle it.

Important data on social media

The stream of social posts, likes, mentions, shares, followers, and many other impressions can clearly prove why big data is important in social media marketing.This is now a must for businesses to collect these tones of information in real-time and analyze it to know how well their social presence is.In fact, every single positive interaction can add to their reputation and any negative feedback can put their efforts at stake.Without the shadow of a doubt, social media marketing analytics, especially for big companies, can’t be impactful without big data. Therefore, many businesses are investing masses of money on big data tools to track real-time consumer behavior across social media.To do this you will have to:

1. Collect the most related data available on social networks
2. Recognize the weight of each data on your target market
3. Convert these data into useful facts and use them in your strategies

Of course, the velocity of your data mining, the volume of the data, and also its variety is of paramount.You’ll need to use the best data technology and analytical tools if you want to leverage big data in marketing effectively.Also, it’s important to consider integrating various social networking sites/apps. Using Facebook, Instagram, Twitter, and LinkedIn will lead to better social interactions and the data from them can represent reality more exactly.

Advantages of big data for digital marketing

A lot of big and small companies are thinking about considerable budgets for big data analytics tools to get ahead of the competition.Here are 5 top benefits a big data analysis approach can bring to your social media campaign:1. Taking care of huge information sourcesAs a social user, you may need to process all data related to your niche that comes from mainstream channels.Analyzing diverse channels is not an easy task and can only be done by using artificial intelligence and big data technology.A lot of business sites allow users to sign up via Google or other main channels. So marketers can collect and analyze data about their niche customers from social networks, browser history, applications, cloud storage, etc.2. Targeting the right audienceIt’s clear that you can’t reach out to all the internet users so you have to narrow down to the most probable group of customers.Thus, social media marketing is all about identifying your target audience. Big data technology has provided marketers with access to insightful data of users’ personal information, photos, favorites, locations, and various kinds of activities.3. Predicting online behaviorsA big data approach can also be used for better decision-making based on previous trends. Data-based businesses are becoming incredibly efficient, as computers can predict the potential choices of customers.In sum, the interests and habits of people can be estimated as they’re changing based on specific overall trends.4. Managing marketing campaignsBig data techniques enable marketers to accurately track the ROI metrics of their social media campaigns.It will provide advertisers with insightful data into how effective a social media campaign has been or can be. Predictive analytical methods greatly help in predicting what products/services consumers want.Tracking consumer behaviors all over social will clear many things about the effectiveness of previous campaigns.This includes media including their engagement and reaction to online advertising. So marketers can optimize their plans for future campaigns to get a higher ROI.5. Identifying fair pricesOne of the biggest problems for marketers is to find reasonable prices for sponsored ads. A lot of different factors are affecting the prices.For example, during the COVID-19 pandemic, a lot of influencers have considerably cut their rates. So, it’s important to track the accepted costs on the internet and make arrangements accordingly.A big data analysis on social media can be helpful to clearly know what prices your competitors or niche customers are agreeing at.

Social media strategy when using big data

If you want to make sure that your big data approach will lead to a higher ROI on social media, you need to have an efficient strategy.Without a strategy, you don’t know what exactly you want from the unstructured data on social channels. Therefore, you’ll waste your time and money without generating considerable leads and boosting sales.A common social media strategy usually comprise the below steps:

• Market research
• Defining SMART goals
• Identifying the audience
• Choosing the right platforms
• Generating relevant content
• Scheduling posts and establishing a social presence
• Engaging with the community
• Influencer marketing
• Analyzing the performance

This strategy should help you achieve certain goals which usually contain below items:

• Drive traffic to your site
• Get higher conversions rates
• Build brand awareness
• Make you appear like a niche leader

Additionally, you’ll need certain metrics to analyze your performance and show you how well you’re achieving these goals.Actually, great use of a big data strategy is to help you know your social performance. You’ll be able to optimize your social techniques based on this information and make the best out of it.

Conclusion

Big data is one of the newest features of information technology and can be of great use to digital marketers. It was tried to summarize the important aspects of big data analytics in social media marketing and the benefits it can bring to your campaign. Remember to take advantage of this technology to optimize your social presence and get ahead of the marketing competition.

The post How Big Data Can Help to Analyze Social Media Performance appeared first on Artificial Intelligence.

The application of artificial intelligence (AI) and machine learning to the business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That’s why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it’s why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors.

In today’s age data science expertise with desirable knowledge in relatable fields is rare to find and therefore we have enlisted top 10 data science experts who you can follow in Twitter.

Hilary Mason

@hmason

Hilary is the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel. Previously, she was the Chief Scientist at bitly. Hilary is also co-founder of HackNY, co-host DataGotham, and is a member of NYCResistor. At Accel, she gets the chance to advise companies large and small on their data strategy. During her 4 years tenure at bitly as a data scientist, she led an amazing team that studied attention on the internet in realtime, doing a mix of research, exploration, and engineering.

Lillian Pierson

@Strategy_Gal

As a data strategy & advisory consultant, Lillian partners with executives & their data teams to provide oversight and recommendations that support winning data initiatives. At Data-Mania, she develops data strategy plans to help executives lead and manage winning data initiatives. Lillian’s strategies guide business leaders to overcome current state obstacles and deliver data initiatives that directly support their unique business visions. Her specialties include Data strategy custom plan development, private VIP days, and in-house workshops; Data strategy, data science, and AI online training courses; Thought leadership services within the data science, artificial intelligence, and Internet-of-Things practice domains; and Business coaching for tech entrepreneurs.

Kira Radinsky

@KiraRadinsky

Kira is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. In the past, she co-founded SalesPredict, acquired by eBay in 2016, and served as eBay director of data science and IL chief scientist. Kira mainly specializes in the field of medical data mining and had the opportunity, while in Microsoft Research, to develop predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics.

Richard Socher

@RichardSocher

Richard Socher is Chief Scientist at Salesforce where he leads the company’s research efforts and works on bringing state of the art artificial intelligence solutions to Salesforce. Prior to Salesforce, Richard was the CEO and founder of MetaMind, a startup acquired by Salesforce in April 2016. Richard was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo! Key Scientific Challenges Award, a Microsoft Research Ph.D. Fellowship in 2012, a 2013 “Magic Grant” from the Brown Institute for Media Innovation, the 2014 GigaOM Structure Award and is currently a member of the WEF Young Global
Leaders Class of 2017.

Christopher Surdak

@CSurdak

Chris is an award-winning author, innovator, disrupter, engineer, and strategist. Currently, he is focused upon the design, implementation, support, and use of Intelligent Automation (IA) technologies such as Robotic Process Automation (RPA), Cognitive Computing (CC), Machine Learning (ML) and Artificial Intelligence (AI). He has authored the books: “The Care and Feeding of BOTS”, “Jerk: Twelve Steps to Rule the World” and “Data Crush: How the Information Tidal Wave Is Creating New Business Opportunities”, winner of GetAbstract’s International Book of the Year, 2014.

Sebastian Thrun

@SebastianThrun

Sebastian Thrun pursues research on robotics, artificial intelligence, education, human-computer interaction, and medical devices. He founded Google’s self-driving car team, after winning the DARPA Grand Challenge. Together with Peter Norvig, Thrun developed the first global MOOC with 160,000 students enrolled. Google Scholar ranks Thrun’s publication h-index 14 worldwide in all of computer science. Thrun also founded Google X, where he founded Google Glass among many other projects. He was elected into the National Academy of Engineering and the German Academy of Sciences at age 39. Thrun founded and sold a number of tech companies, including Udacity (valued at over $1B) and KittyHawk. Fast Company named Thrun the fifth most creative person in business, and Foreign Policy touted him Global Thinker 4.

John Myles White

@johnmyleswhite

John Myles White manages the newly founded New York City branch of Facebook’s Core Data Science team. Prior to coming to Facebook, John worked at MIT, where he helped develop data analysis libraries for the Julia programming language. Before going to MIT, John completed a Ph.D. in psychology in Princeton, where he studied behavioral economics. John is also the author of two O’Reilly books: Machine Learning for Hackers and Bandit Algorithms for Website Optimization.

Dean Abbott

@deanabb

Dean Abbott is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, response modeling, survey analysis, planned giving, predictive toxicology, signal process, and missile guidance. Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals.

Kenneth Cukier

@kncukier

Kenneth Cukier is a Senior Editor at The Economist, and host of its weekly podcast on technology. He is also an associate fellow at Said Business School at Oxford, researching artificial intelligence. Kenn is the co-author of “Big Data: A Revolution That Transforms How We Live, Work, and Think” with Viktor Mayer-Schönberger. It was an NYT Bestseller translated in over 20 languages and sold over 1 million copies worldwide. It won the National Library of China’s Wenjin Book Award and was a finalist for the FT Business Book of the Year. Kenn co-authored a follow-on book, “Learning with Big Data: The Future of Education”

Nando de Freitas

@NandoDF

Nando de Freitas is a Professor of Computer Science at Oxford University. He was a tenured Full Professor in Computer Science at UBC until 2014. Currently, he is an adjunct professor at this department. He is also a Fellow of the Canadian Institute for Advanced Research (CIFAR). As a visiting post-doctoral scholar with Stuart Russell at UC Berkeley, he worked on machine learning, computer vision, image retrieval, probabilistic models in artificial intelligence, variational inference algorithms, particle filtering, and MCMC simulation. Nando de Freitas has a Ph.D. on Bayesian methods for neural networks (Trinity College, Cambridge University).

The post TOP 10 DATA SCIENCE EXPERTS TO FOLLOW ON TWITTER appeared first on Artificial Intelligence.

Twitter is now using machine learning to automatically crop picture previews to their most interesting part. The use of machine learning and neural networks for image recognition as become common with both Google and Facebook heavily deploying the technology on their platforms. Now, Twitter is also joining with its own implementation with a single aim of improving user experience.

Twitter explains that it has been working on this technology for a while now and describes the methods in a detailed blog post. Machine Learning Researcher Lucas Theis and Software Engineer Zehan Wang explain that the micro-blogging platform previously used face detection to focus the image on the most prominent face it could find. However, since all the images don’t have faces, the company found the approach didn’t work with all kinds of pictures especially objects and cats.

Twitter has now tweaked its algorithm with what it calls as cropping using saliency. The platform now crops images based on whatever is interesting and not just limited to faces. Twitter defines the most interesting part as a region having high saliency where a person is likely to look at when freely viewing the image.

“In general, people tend to pay more attention to faces, text, animals, but also other objects and regions of high contrast.”

Twitter now uses this data to train its neural networks and other algorithms to predict what people might want to look at rather than limiting it to prominent face. Engineers at Twitter note that recent advances in machine learning has made saliency prediction a lot better. The major challenge for the company being the sllow processing speed of neural networks deployed to predict saliency, making them not suitable to run in production.

However, Twitter optimized the neural network’s implementation by using two techniques that help reduce the size and hence the computational requirements. Twitter explains using a technique called knowledge distillation to train a similar network in order to imitate the slower but more powerful network. The second is a pruning technique to remove feature maps of the neural network which were costly to compute but contributed in little to performance.

In raw performance terms, Twitter claims to have made its neural networks 10x faster than a vanilla implementation. The update is in the process of being rolled out to everyone accessing the service on web, iOS and Android apps. So, next time if you see a cat prominently on your Twitter feed then it is powered by these very neural networks.

The post Twitter now uses machine learning to auto-crop images smartly appeared first on Artificial Intelligence.