Information Diffusion

During a crisis, authorities need to effectively disseminate information. We address the problem of deciding how crisis-related information should be published on Facebook to reach as many people as possible. We examine three recent terrorist attacks in Berlin, London and Stockholm. Our specific focus lies with official Facebook pages by municipalities and emergency service agencies. We collected posts about the events, along with the number of shares, likes and emotional reactions to them. In a regression analysis, several variables were examined that capture decisions on which information to publish and how. Posts containing condolences were found to result in three times as many emotional reactions as other posts, all other variables held constant. Images and videos positively affected the number of reactions by factors of 2.2 and 3.9, respectively, while text length negatively affected the number of shares. These results will help in the development of effective guidelines.

Warning messages are being discussed as a possible mechanism to contain the circulation of  false  information  on  social  media.  Their  effectiveness  for  this  purpose,  however,  is unclear.  This  article  describes  a survey experiment  carried  out  to  test  two  designs  of warning  messages:  a  simple  one  identical  to  the  one  used  by  Facebook,  and  a  more complex one informed by recent research. We find no evidence that either design is clearly superior  to not  showing  a  warning  message.  This  result  has  serious  implications  for brands  and  politicians,  who  might  find  false  information  about  them  spreading uncontrollably, as well as for managers of social media platforms, who are struggling to find effective means of controlling the diffusion of misinformation.

The purpose of this research is to investigate negativity bias in secondary electronic word-of-mouth (eWOM). Two experiments, one laboratory and one field, were conducted to study actual dissemination behavior. The results demonstrate a strong tendency toward the negative in the dissemination of secondary commercial information. In line with Dynamic Social Impact Theory, our findings show that consumers disseminate online negative content to more recipients, for a longer period of time and in more elaborated and assimilated manner than they do positive information. The research is important from both a theoretical and managerial perspective. In the former, it enriches existing literature on eWOM by providing insight into theoretical dimensions of the negativity theory not examined before (duration, role of valence, elaboration, and assimilation). Findings provide managerial insights into designing more effective WOM and publicity campaigns.

Like  many  other  industries,  the  media  industry  has  been  profoundly  affected  by  social media. The public discuss the news on services such as Twitter, and some celebrity tweets are picked up by journalists and become news stories themselves. Information systems research into information diffusion has largely neglected these cross-platform diffusion patterns. To address this gap, the presented research in progress examines indicators of information  spill-over  between  Twitter  and  ten  German  news  websites,  by  detecting references  in  the  form  of  URLs  and  mentions  on  both  sides.  Furthermore,  the  paper presents  automatic  and  manual  methods  to  identify  two  categories  of  spill-over, reference  spill-over  and  content  spill-over.  Preliminary  findings  reveal  differences between news outlets in how frequently they are referenced by Twitter users, and in how often they reference Twitter in their own stories.

Little is currently known about the factors that
promote  the  propagation  of  information  in  online  social
networks following terrorist events. In this paper we took
the case of the terrorist event in Woolwich, London in 2013
and built models to predict information flow
size
and
sur-
vival
using data derived from the popular social networking
site Twitter. We define information flows as the propaga-
tion  over  time  of  information  posted  to  Twitter  via  the
action of retweeting. Following a comparison with differ-
ent predictive methods, and due to the distribution exhib-
ited  by  our  dependent
size
measure,  we  used  the  zero-
truncated  negative  binomial  (ZTNB)  regression  method.
To model
survival
, the Cox regression technique was used
because  it  estimates  proportional  hazard  rates  for  inde-
pendent  measures.  Following  a  principal  component  ana-
lysis  to  reduce  the  dimensionality  of  the  data,  social,
temporal  and  content  factors  of  the  tweet  were  used  as
predictors  in  both  models.  Given  the  likely  emotive
reaction  caused by the event, we emphasize the influence
of  emotive  content  on  propagation  in  the  discussion  sec-
tion. From a sample of Twitter data collected following the
event (
N
=
427,330) we report novel findings that identify
that  the  sentiment  expressed  in  the  tweet  is  statistically
significantly predictive of both size and survival  of infor-
mation  flows  of  this  nature.  Furthermore,  the  number  of
offline press reports relating to the event published on the
day the tweet was posted was a significant predictor of size,
as was the tension expressed in a tweet in relation to sur-
vival. Furthermore, time lags between retweets and the co-
occurrence  of  URLS  and  hashtags  also  emerged  as
significant