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Researchers Develop Algorithm to Predict an Actor’s Career

Hence, an actor’s success could be down to their circumstances rather than the acting ability

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Mathematics. Source: Pixabay

Researchers have developed an algorithm that predicts whether an actor’s career has peaked and also predicts their most successful days in future, with an accuracy of 85 per cent.

The research team discovered that the most productive years for actors, defined as the year with the largest number of credited jobs, are towards the beginning of their careers.

The study, published in the journal Nature Communications shows how around 70 per cent actors and actresses have careers that only last for one year.

“Our results shed light on the underlying social dynamics taking place in show business and raise questions about the fairness of the system. Our predictive model for actors is also far from the randomness that is displayed for scientists and artists,” study author from the Queen Mary University of London Oliver Williams said.

For the study, the researchers used data of Internet Movie Database (IMDb) and looked at the careers of over 2.4 million actors from around the world from 1888 to 2016 to analyse and predict success on the silver screen.

They found that careers are clustered into ‘hot’ and ‘cold’ streaks, as individuals do not tend to work at a steady rate in a business where unemployment rates hover at around 90 per cent.

New Algorithm That may Predict Your Intelligence
New Algorithm that may Predict actor’s career. (IANS)

There is also huge evidence of gender biases in the industry, as most of the patterns were observed different for actors and actresses, the study said.

According to the outcomes, the total number of jobs in a career is underpinned by the rich-get-richer phenomenon.

What is interesting about this observation is that the rich-get-richer effects are well known to develop out of arbitrary and unpredictable random events that get amplified.

Also Read- Motherhood Teaches Women to Become Comfortable with Their Bodies: Study

Hence, an actor’s success could be down to their circumstances rather than the acting ability.

“We think the approach and methods developed in this paper could be of interest to the film industry: for example, they could provide complementary data analytics to IMDb. This does also bring with it a number of open questions,” said Lucas Lacasa from the Queen Mary University of London. (IANS)

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Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

twitter, white swan, suicide, awareness
Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

Also Read: Facebook Announces Some New Features to its Video Capabilities

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)