THERE HAS BEEN a phenomenal increase in the use of online social media (OSM) services in India, including Facebook, Twitter, Instagram, LinkedIn, and YouTube. In addition to these services, one-to-one messaging services like WhatsApp have 200 million users, the highest in the world. India has 462 million users accessing the Internet, among these: Facebook has 250+ million users, LinkedIn 42+ million, and Twitter 23+ million users, and the majority of users access these services through their mobile phones. These services have had a profound impact in India—overall digital literacy has increased, people are more connected, dissemination of local language content has increased, information exchanged during crises is substantial, and more. The deep penetration of social media services also has negative effects—the propagation of false information and hate, an increase in spammers and phishers, users are losing social skills, and more. Newness of technology/mobile phones, lowliteracy rates, and cheaper mobile data rates are cited as negative impacts of social media services on society. Research has been mainly directed toward regulation of content generated on OSM. It can be classified in the following categories: ˲ Identifying topical interests and expertise of the users in online behavior and efficiently matching the consumers and producers of content; DOI:10.1145/3345671
[1]
Niloy Ganguly,et al.
Learning and Forecasting Opinion Dynamics in Social Networks
,
2015,
NIPS.
[2]
Krishna P. Gummadi,et al.
On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs
,
2016,
CSCW.
[3]
Niloy Ganguly,et al.
Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks
,
2018,
WWW.
[4]
Niharika Sachdeva,et al.
Call for Service: Characterizing and Modeling Police Response to Serviceable Requests on Facebook
,
2017,
CSCW.
[5]
Niloy Ganguly,et al.
Extracting and Summarizing Situational Information from the Twitter Social Media during Disasters
,
2018,
ACM Trans. Web.
[6]
Muhammad Imran,et al.
Identifying Sub-events and Summarizing Disaster-Related Information from Microblogs
,
2018,
SIGIR.
[7]
Monojit Choudhury,et al.
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
,
2018,
ACL.
[8]
Krishna P. Gummadi,et al.
Deep Twitter diving: exploring topical groups in microblogs at scale
,
2014,
CSCW.
[9]
Krishna P. Gummadi,et al.
Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations
,
2017,
ICWSM.
[10]
Niloy Ganguly,et al.
A Deep Generative Model for Code-Switched Text
,
2019,
IJCAI.