Informs 2015

321 / 552

INFORMS Philadelphia – 2015
319
TC04
04-Room 304, Marriott
Social Media Analytics Best Papers Finalist
Competition
Cluster: Social Media Analytics
Invited Session
Chair: Theodore Allen, Associate Professor, The Ohio State University,
1971 Neil Avenue, 210 Baker Systems, Columbus, OH, 43221, United
States of America,
1 - Social Media Analytics: The Effectiveness of Marketing Strategies
in Online Social Media
Vilma Todri, PhD Candidate In Information Systems, NYU, 44 W
4th St, KMC Room 8-181, New York, NY, 10012, United States of
America, , Panagiotis Adamopoulos
This paper studies a novel social media venture and seeks to understand the
effectiveness of marketing strategies in social media platforms by evaluating their
impact on participating brands. We use a real-world data set and employ a
promising research approach combining econometric with predictive modeling
techniques.
2 - Predicting Crowd Behavior with Big Public Data
Nathan Kallus, MIT, 77 Massachusetts Ave., E40-149, Cambridge,
MA, 02139, United States of America,
We present efforts to predict the occurrence, specific timeframe, and location of
crowd actions before they occur based on public data collected from over 300,000
open content web sources in 7 languages, from all over the world, ranging from
mainstream news to government publications to blogs and social media.
3 - Participation vs. Effectiveness of Paid Endorsers in Social
Advertising Campaigns: A Field Experiment
Jing Peng, The Wharton School, University of Pennsylvania,
3730 Walnut Street Suite 500, Philadelphia, PA, 19104,
United States of America, ,
Christophe Van den Bulte
We investigate the participation and effectiveness of paid endorsers in viral-for-
hire social advertising. Specifically, we investigate (i) how financial incentives
affect potential endorsers’ participation and effectiveness in generating online
engagements (likes, comments, and retweets), and (ii) which network
characteristics and prior engagement characteristics are associated with
participation and effectiveness. We conduct a large scale field experiment with an
invitation design in which we manipulate both financial incentives and the soft
eligibility requirement to participate in the campaign. The latter provides a strong
and valid instrument to separate participation from outcomes effects. Since likes,
comments, and retweets are count variables, and since potential endorsers can
self-select to participate in multiple campaigns we ran, we use a Poisson
lognormal model with sample selection and correlated random effects to analyze
variations in participation and effectiveness. There are three main findings. (1)
Financial incentive did not affect either participation or effectiveness. (2) Potential
endorsers more likely to participate are often less effective. (3) Which
characteristics are associated with effectiveness depends on whether success is
measured in likes, comments, or retweets. Our findings provide new insights on
how marketers can improve social advertising campaigns by better targeting and
incenting potential endorsers.
4 - A Visual Monitoring Technique Based on Importance Score and
Twitter Feeds
Zhenhuan Sui, Graduate Research Assistant, The Ohio State
University, 2501 Gardenia Drive, Columbus, Oh, 43235, United
States of America, David Milam, Theodore Allen
We propose a visual monitoring technique based on topic models, a point system,
and Twitter feeds for monitoring. The method generates a chart showing the
important and trending topics that are discussed over a given time period which is
illustrate the methodology using cyber-security cases.
TC05
05-Room 305, Marriott
Social Media Impact
Cluster: Social Media Analytics
Invited Session
Chair: Les Servi, The MITRE Corporation, 202 Burlington Road,
Bedford, MA, United States of America,
1 - Big Data Means Big PR: A Review of News Coverage of Big Data
in the Popular Press
Amir Gandomi, Assistant Professor, Ryerson University,
350 Victoria Street, Toronto, ON, M5B 2K3, Canada,
, Murtaza Haider
In this paper, we undertake a content analysis of the news feed regarding big data
analytics. We develop a corpus of related news items. We code the corpus and
prepare it for analysis by the Natural Language Processing (NLP) algorithms. Our
purpose is to conduct a systematic analysis of the news contents to determine the
primary themes being projected by the proponents of big data. We will explore
ways to identify real value content that may help to draw meaningful inferences.
2 - Modeling Message Dissemination in Social Media for
Emergency Communication
Justin Yates, Professor, Francis Marion University,
4822 E Palmetto St, Florence, SC, United States of America,
, Xin Ma
We introduce the Single-network Multi-message Social Media Message
Dissemination Problem (SM-SMMDP) as a discrete optimization model to
examine message dissemination in social media and to explore message
dissemination strategies for government and non-government emergency
management agencies. We implement a detailed computational experiment on
representative small-scale networks and discuss implications for real-world
application.
3 - Analyzing and Predicting Threatening Language in
#gamergate Tweets
Cheyanne Baird, Senior Linguistic Specialist, SAS Institute Inc.,
Boston Regional Office, Prudential Tower, 800 Boylston St.,
Suite 2200, Boston, MA, 02199, United States of America,
, Michael Wallis, Praveen Lakkaraju
Aggressive language in online communities can thrive, escalate, and signal
palpable threat. This is apparent in #gamergate tweets. Using SAS Text Analytics,
we will show how language transitions from negative sentiment to threatening
language in #gamergate tweets, building a model to predict probable threat in
social media.
TC06
06-Room 306, Marriott
Dynamics and Information in Commodity Markets
Sponsor: Financial Services
Sponsored Session
Chair: Richard Sowers, Professor, University of Illinois at Urbana-
Champaign, Urbana, IL, 61801, United States of America,
1 - Model Uncertainty in Commodity Markets
Sebastian Jaimungal, University of Toronto,
Department of Statistical Sciences, Toronto, Canada,
¡lvaro Cartea, Zhen Qin
This paper studies the effect that model ambiguity in commodities have on the
value of derivative contracts. The base model consists of three drivers: a mean-
reverting diffusion, a mean-reverting jump, and a stochastic volatility component.
We allow the agent to consider a wide class of alternate models, and penalize the
differing components of the model individually. We demonstrate how agents alter
their behavior in the presence of ambiguity and how derivatives and spreads are
affected by it.
2 - Index Tracking with Futures
Brian Ward, Columbia University, New York, NY,
Tim Leung
Exchange Traded Funds (ETF) market continues to grow. Many ETFs are designed
to track an index, and their leveraged benchmarks (2x, 3x, etc.) Since many of
these indices are not directly traded, we consider tracking them using futures of
various maturities. We do so both statically and dynamically, and backtest our
strategies with empirical data.
3 - Investment in Commodities ETFs and Management of Contango
Andrew Papanicolaou, NYU Polytechnic, 6 Metrotech Center,
Brooklyn, Ny, 11201, United States of America,
The last two decades have seen growing investment in commodities markets.
Commodities ETFs are popular but not a passive investment, as they will post
losses in contango markets. The focus of this talk will be storable commodities,
where uncertainty in the convenience yield reduces the Sharpe ratios. Losses are
seen as an information premium, which is quantified through a Merton-type
control problem.
TC06