Use of artificial intelligence to analyze social networks on the web

A social network is a social arrangement of actors who make up a group, within a network; there can be an array of ties and nodes that exemplifies common occurrences within a network and common relationships. Lui (2011), describes a social network as, “the study of social entities (people in organization, called actors), and their interactions and relationships. The interactions and relationships can be represented with a network or graph, where each vertex (or node) represents an actor and each link represents a relationship.” At the present time there is a growth in virtual social networking with the common emergence of social networks being replicated online, for example social networking sites such as Twitter, Facebook and LinkedIn. From a marketing perspective, analysis and simulation of these networks can help to understand consumer behavior and opinion. The use of Agent-based social simulation techniques and data/opinion mining to collect social knowledge of networks can help a marketer to understand their market and segments within it.

Social computing

Social computing is the branch of technology that can be used by marketers to analyze social behaviors within networks and also allows for creation of artificial social agents. Social computing provides the platform to create social based software; some earlier examples of social computing are such systems that allow a user to extract social information such as contact information from email accounts e.g. addresses and companies titles from ones email using Conditional Random Field (CRFs) technology.

Data mining

Data mining involves searching the Web for existing information namely opinions and feelings that are posted online among social networks. “ This area of study is called opinion mining or sentiment analysis. It analyzes peoples opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics, and their attributes”. However searching for this information and analysis of it can be a sizeable task, manually analyzing this information also presents the potential for researcher bias. Therefore, objective opinion analysis systems are suggested as a solution to this in the form of automated opinion mining and summarization systems. Marketers using this type of intelligence to make inferences about consumer opinion should be wary of what is called opinion spam, where fake opinions or reviews are posted in the web in order to influence potential consumers for or against a product or service.

Search engines are a common type of intelligence that seeks to learn what the user is interested in to present appropriate information. PageRank and HITS are examples of algorithms that search for information via hyperlinks; Google uses PageRank to control its search engine. Hyperlink based intelligence can be used to seek out web communities, which is described as ‘ a cluster of densely linked pages representing a group of people with a common interest’.

Centrality and prestige are types of measurement terms used to describe the level of common occurrences among a group of actors; the terms help to describe the level of influence and actor holds within a social network. Someone who has many ties within a network would be described as a ‘central’ or ‘prestige’ actor. Identifying these nodes within a social network is helpful for marketers to find out who are the trendsetters within social networks.

Social Media AI-based tools

Ellott (2017) looked at the AI based tools that are transforming social media markets. There are six areas of social media marketing that are being impacted by AI:

1. content creation,
2. consumer intelligence,
3. customer service,
4. influencer marketing,
5. content optimization, and
6. competitive intelligence.

One tool, Twizoo, uses AI to gather reviews from social networking sites about restaurants to help users find a place to eat. Twizoo had much success from the feedback of their users and expanded by launching “a widget where travel and hospitality websites could instantly bring those social media reviews to their own audiences” (Twizzo, 2017).

Influencer marketing is huge on social media. Many brands collaborate and sponsor popular social media users and try to promote their products to that social media user’s followers. This has been a huge tactic for Sugar Bear Hair and Fab Fit Fun. One company, InsightPool, uses AI to search through over 600 million influencers on social media to find the influencers who fit the brand’s personality and target audience (Ellot, 2017). This can be an effective tool when searching for new influencers or a specific audience. It could also be cost effective to find someone who is not famous (like Kardashians/Bachelorette cast) but could also influence a large audience and bring in sales

Media and e-commerce

Some AI applications are geared towards the analysis of audiovisual media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision, which is a major application area of AI.

Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces. The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.

Media analysis AI companies often provide their services over a REST API that enables machine-based automatic access to the technology and allows machine-reading of the results. For example, IBM, Microsoft, Amazon and the video AI company Valossa allow access to their media recognition technology by using RESTful APIs.

AI is also widely used in E-commerce Industry for applications like Visual search, Visually similar recommendation, Chatbots, Automated product tagging etc. Another generic application is to increase search discoverability and making social media content shoppable.

Music

While the evolution of music has always been affected by technology, artificial intelligence has enabled, through scientific advances, to emulate, at some extent, human-like composition.

Among notable early efforts, David Cope created an AI called Emily Howell that managed to become well known in the field of Algorithmic Computer Music. The algorithm behind Emily Howell is registered as a US patent.

The AI Iamus created 2012 the first complete classical album fully composed by a computer.

Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonic music, mainly classical music for film scores. It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.

Artificial intelligences can even produce music usable in a medical setting, with Melomics’s effort to use computer-generated music for stress and pain relief.

Moreover, initiatives such as Google Magenta, conducted by the Google Brain team, want to find out if an artificial intelligence can be capable of creating compelling art.

At Sony CSL Research Laboratory, their Flow Machines software has created pop songs by learning music styles from a huge database of songs. By analyzing unique combinations of styles and optimizing techniques, it can compose in any style.

Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style. Since the software has been open sourced musicians, such as Taryn Southern[50] have been collaborating with the project to create music.

News, publishing and writing

The company Narrative Science makes computer-generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English. It also creates financial reports and real estate analyses. Similarly, the company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football. The company is projected to generate one billion stories in 2014, up from 350 million in 2013.

Echobox is a software company that helps publishers increase traffic by 'intelligently' posting articles on social media platforms such as Facebook and Twitter. By analysing large amounts of data, it learns how specific audiences respond to different articles at different times of the day. It then chooses the best stories to post and the best times to post them. It uses both historical and real-time data to understand to what has worked well in the past as well as what is currently trending on the web.

Another company, called Yseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in natural language. Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French & German.

Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader. It's like hiring a personal editor for each individual reader to curate the perfect reading experience.

IRIS.TV is helping media companies with its AI-powered video personalization and programming platform. It allows publishers and content owners to surface contextually relevant content to audiences based on consumer viewing patterns.

Beyond automation of writing tasks given data input, AI has shown significant potential for computers to engage in higher-level creative work. AI Storytelling has been an active field of research since James Meehan's development of TALESPIN, which made up stories similar to the fables of Aesop. The program would start with a set of characters who wanted to achieve certain goals, with the story as a narration of the characters’ attempts at executing plans to satisfy these goals. Since Meehan, other researchers have worked on AI Storytelling using similar or different approaches. Mark Riedl and Vadim Bulitko argued that the essence of storytelling was an experience management problem, or "how to balance the need for a coherent story progression with user agency, which are often at odds."

While most research on AI storytelling has focused on story generation (e.g. character and plot), there has also been significant investigation in story communication. In 2002, researchers at North Carolina State University developed an architectural framework for narrative prose generation. Their particular implementation was able faithfully reproduced text variety and complexity of a number of stories, such as red riding hood, with human-like adroitness. This particular field continues to gain interest. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize

Online and telephone customer service

Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages. It can avail for enterprises to reduce their operation and training cost. A major underlying technology to such systems is natural language processing. Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.

Major companies are investing in AI to handle difficult customer in the future. Google's most recent development analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.

Power electronics

Power electronics converters are an enabling technology for renewable energy, energy storage, electric vehicles and high-voltage direct current transmission systems within the electrical grid. These converters are prone to failures and such failures can cause downtimes that may require costly maintenance or even have catastrophic consequences in mission critical applications. Researchers are using AI to do the automated design process for reliable power electronics converters, by calculating exact design parameters that ensure desired lifetime of the converter under specified mission profile.

Sensors

Artificial Intelligence has been combined with many sensor technologies, such as Digital Spectrometry by IdeaCuria Inc. which enables many applications such as at home water quality monitoring.

Telecommunications maintenance

Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20,000 engineers.

Toys and games

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

Companies like Mattel have been creating an assortment of AI-enabled toys for kids as young as age three. Using proprietary AI engines and speech recognition tools, they are able to understand conversations, give intelligent responses and learn quickly.

AI has also been applied to video games, for example video game bots, which are designed to stand in as opponents where humans aren't available or desired.

Transportation

Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg[citation needed] and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of ?koda variants (?koda Fabia) also currently include a Fuzzy Logic-based controller.

Today's cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent. In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives. Due to this high degree of complexity of the transportation, and in particular the automotive, application, it is in most cases not possible to train an AI algorithm in a real-world driving environment. To overcome the challenge of training neural networks for automated driving, methodologies based on virtual development resp. testing toolchains have been proposed.