We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.

There are plenty of ways to suppressing the bad links, all of which are very manageable and very effective:

• Setting up new social media profiles and be active on all of them.

• Starting a company blog and posting incredible content routinely.

• Keep publishing other types of media, like podcasts, images, and videos.

• Refresh content you already have on the web, like pages on your company site.

• Leaving comments on other influential websites within your industry.

• Become a thought leader by publishing expert advice in professional forums.

• Create quality content and link your content from a variety of authoritative websites.