Under UCWeb's We-Media Reward Plan, 1,000 content writers will be recruited in India and Indonesia who will be able to earn at least Rs 50,000 per month through the UC News platform.
UCWeb said in a statement here that the integrated evaluation for content writers "will be done under 4 key indicators including page views, content customization, content category and publishing frequency".
"The UC News Content team will review quality and the overall influence of the candidate while keeping in mind content originality and legitimacy before giving the final confirmation."
Alibaba Mobile Business Group President He Xiaopeng, the co-founder of UCWeb, launched the We-Media Reward Plan 2 here -- with an initial investment of Rs 5 crore, as part of its plan to invest Rs 200 crore over the next two years to drive content distribution in the country.
"The programme has received more than 1,300 applications since its launch in March 2017. One of the first writers to qualify for the program is CricketTrolls, a blog offering latest cricket news, offbeat news, memes and more with an aim to make cricket more fun," the statement said.
"The blog enjoys over 3.62 million page views and is earning more than $900 per month. Besides an upgrade in Ad revenue sharing model, the We-Media Reward Plan 2.0 program will open the door of opportunity to the most talented writers in the country," it added.
In June 2014, UCWeb was integrated into the Alibaba Group in China's biggest internet acquisition. It is hoping to emerge as the largest content generation and service platform in India in 2017.
(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)
Doraiah Chowdary Vundavally is a Software engineer at VTech . He is the news editor of SocialNews.XYZ and Freelance writer-contributes Telugu and English Columns on Films, Politics, and Gossips. He is the primary contributor for South Cinema Section of SocialNews.XYZ. His mission is to help to develop SocialNews.XYZ into a News website that has no bias or judgement towards any.
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